| title: “Single-celled bottlenecks, germlines and the evolution of complex multi-cellularity” |
| output: |
| output: rmarkdown::github_document |
Pre-amble
load packages
library(ape)
library(ggplot2)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.0 ──## ✓ tibble 3.0.4 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.0
## ✓ purrr 0.3.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(knitr)
library(brms) #https://rdrr.io/cran/brms/f/vignettes/brms_phylogenetics.Rmd## Loading required package: Rcpp## Loading 'brms' package (version 2.15.0). Useful instructions
## can be found by typing help('brms'). A more detailed introduction
## to the package is available through vignette('brms_overview').##
## Attaching package: 'brms'## The following object is masked from 'package:stats':
##
## arlibrary(tidybayes)##
## Attaching package: 'tidybayes'## The following objects are masked from 'package:brms':
##
## dstudent_t, pstudent_t, qstudent_t, rstudent_tlibrary(rotl) #see https://cran.r-project.org/web/packages/rotl/vignettes/rotl.htmlread in data
df<- read.csv('data/germline_data_1.1.csv')create phylogeny files
ResolvedNames <- tnrs_match_names(df$species.updated.rotl, context_name = 'All life') #search for similar names in the 'open tree of life' project (ROTL)
ResolvedNames$IsInTree <- is_in_tree(ResolvedNames$ott_id) #T/F, did the above find a match that can be put in the phylogeny?
ResolvedNamesInTree<- subset(ResolvedNames, IsInTree==T) #subset to only those where present in the phylogeny
AllTree<- tol_induced_subtree(ResolvedNamesInTree$ott_id, label_format = 'id') #draw the phylogeny## Warning in collapse_singles(tr, show_progress): Dropping singleton nodes
## with labels: mrcaott2ott142555, mrcaott2ott7623, ott916750, mrcaott2ott50189,
## mrcaott2ott108668, mrcaott2ott59852, mrcaott2ott8171, ott10210, ott99252,
## mrcaott2ott2645, mrcaott2ott35778, ott5298374, mrcaott2ott10930,
## mrcaott2ott2441, mrcaott2ott969, mrcaott2ott62529, mrcaott2ott8379,
## ott431495, ott853757, ott5316182, mrcaott248ott10053, mrcaott248ott20991,
## mrcaott248ott557, mrcaott557ott67236, ott216628, mrcaott557ott717698,
## mrcaott557ott864011, mrcaott557ott37775, mrcaott557ott904, mrcaott904ott121240,
## mrcaott904ott264912, mrcaott904ott8870, mrcaott904ott52717, ott33109,
## mrcaott904ott63159, mrcaott904ott96612, mrcaott904ott31366, mrcaott904ott9799,
## ott584111, mrcaott904ott5005, ott227063, ott801070, ott226780, ott1058517,
## ott5308424, ott225270, mrcaott252ott213153, ott921871, mrcaott252ott128594,
## mrcaott252ott1477, mrcaott1477ott591692, mrcaott1477ott2066, ott105574,
## ott423248, ott423246, ott215123, ott568878, mrcaott334ott335, ott852744,
## ott857751, mrcaott3355ott25934, mrcaott3355ott3511, mrcaott3355ott55292,
## mrcaott3355ott4623, mrcaott3355ott579768, mrcaott3355ott3515,
## mrcaott3355ott3520, mrcaott3520ott4662, mrcaott4662ott12148, ott361626,
## mrcaott4662ott285937, mrcaott4662ott913602, ott792012, mrcaott1439ott109938,
## mrcaott1439ott12986, ott695980, ott643238, mrcaott4474ott13510,
## ott17708, mrcaott13510ott198928, mrcaott13510ott84425, ott597423,
## mrcaott84425ott310604, mrcaott84425ott523888, ott597424, ott481972,
## mrcaott290ott3983, ott1085578, ott1008186, mrcaott338992ott338994,
## mrcaott338994ott431388, mrcaott66494ott407568, mrcaott407568ott432842,
## mrcaott407568ott1059896, mrcaott407568ott1059891, mrcaott1059891ott1059892,
## mrcaott1059891ott1059898, mrcaott1059895ott1059900, mrcaott5202ott159280,
## mrcaott5202ott30666, mrcaott5202ott40102, ott1058522, ott5296507,
## mrcaott237ott8444, ott4736806, ott994067, mrcaott8444ott9094,
## mrcaott8444ott62995, mrcaott8444ott8454, ott771683, mrcaott61771ott212475,
## mrcaott212475ott568647, mrcaott212475ott263467, mrcaott212475ott225275,
## mrcaott225275ott4732813, mrcaott225275ott225278, ott166292, mrcaott784ott7848,
## ott5662210, mrcaott784ott5624, ott645153, ott915107, mrcaott56553ott1005825,
## mrcaott56553ott56558, mrcaott56558ott665943, mrcaott56558ott141635,
## mrcaott56558ott118926, mrcaott118926ott141640, mrcaott141640ott141652,
## mrcaott141640ott141654, mrcaott141654ott186999, mrcaott186999ott476715,
## ott687390, ott687388, ott7045109, mrcaott79117ott203759, mrcaott203759ott283126,
## mrcaott283126ott733462, ott3866637, mrcaott113196ott932011,
## mrcaott113196ott178177, ott7045107, ott178176, ott738980, ott1045377,
## ott108839, ott164381, ott164382, ott56601, ott991930, ott854252,
## mrcaott82773ott443123, mrcaott82773ott126784, ott1085640, ott633609,
## mrcaott82773ott220656, ott237153, mrcaott82773ott82774, mrcaott82773ott283283,
## mrcaott82773ott108923, mrcaott108923ott770042, mrcaott108923ott890929,
## ott991932, ott449116, mrcaott15887ott15911, mrcaott15911ott16002,
## mrcaott54768ott230405, mrcaott54768ott311663, mrcaott54768ott126782,
## ott925636, ott177580, mrcaott108927ott199292, ott199294, ott199293,
## mrcaott199292ott504251, mrcaott177579ott230401, mrcaott230401ott230413,
## ott230416, ott230418, mrcaott1066ott204205, ott821346, mrcaott1066ott86152,
## mrcaott1066ott1769, mrcaott1769ott7813, mrcaott7813ott148635,
## mrcaott7813ott86148, mrcaott7813ott454749, mrcaott7813ott86154, ott765282,
## mrcaott79119ott118326, ott235116, ott821351, ott821352, mrcaott118326ott243429,
## mrcaott118326ott204209, mrcaott118326ott145724, mrcaott118326ott192501,
## ott905781, mrcaott199296ott430948, ott821354, mrcaott3228ott6999,
## ott736338, ott738997, ott738998, ott235114, ott738989, mrcaott185ott42071,
## mrcaott185ott1426, mrcaott1426ott1544, mrcaott1544ott8659, mrcaott1544ott15345,
## mrcaott1544ott9282, mrcaott9389ott818260, mrcaott9389ott23557,
## ott522747, ott60478, mrcaott130653ott849026, mrcaott130653ott633717,
## mrcaott633708ott1061934, mrcaott419170ott958293, mrcaott448158ott1061937,
## ott533335, ott256059, ott250579, ott878953, mrcaott1551ott80201,
## mrcaott1551ott142559, mrcaott1551ott10831, ott674036, mrcaott1551ott36814,
## mrcaott1551ott253799, mrcaott3043ott12378, mrcaott3043ott7826,
## mrcaott3043ott16188, mrcaott3043ott686586, mrcaott686586ott743093, ott743087,
## ott409810, ott121428, ott733207, ott187107, ott238999, ott359292, ott779027,
## ott4013685, ott4741359, ott743091, ott219464, mrcaott7358ott9920, ott207133,
## mrcaott148ott902, mrcaott148ott7555, ott266745, mrcaott148ott105353,
## mrcaott148ott720, mrcaott148ott2378, mrcaott2378ott6033, mrcaott2378ott58215,
## mrcaott2378ott15569, mrcaott2378ott4029549, mrcaott2378ott161118, ott9570,
## ott9571, ott466981, ott727980, mrcaott287738ott994601, mrcaott287738ott649193,
## ott649199, mrcaott649193ott740531, ott657906, ott388859, ott910940,
## ott388863, ott266749, ott266750, ott263979, ott263986, ott48627, ott48626,
## mrcaott110926ott189736, mrcaott110926ott883194, mrcaott110926ott437350,
## ott736730, ott34569, mrcaott34560ott48612, mrcaott48612ott497066,
## mrcaott48612ott72521, ott878348, ott878353, ott263989, ott83429, ott189267,
## ott736724, ott945531, ott2821098, ott632600, ott5251280, ott817799,
## mrcaott165187ott342594, ott213049, ott213048, ott171425, ott736726, ott736727,
## ott48611, ott48625, ott48623, ott167448, ott388861, ott275890, ott92555,
## ott92554, ott613724, ott92557, mrcaott128669ott241774, ott151001, ott1004458,
## ott5585508, ott266751, ott302424, ott340382, mrcaott1546ott1671, ott16119,
## mrcaott1671ott16129, ott16124, ott232934, ott129120, ott5257371, ott735252,
## ott2927065, mrcaott276ott5110, mrcaott5110ott15859, mrcaott15859ott74636,
## ott120239, ott5248808, ott1011295, ott765114, ott1064655, mrcaott3973ott26103,
## mrcaott26103ott273110, mrcaott26103ott229626, ott4008839, ott591695, ott7041852,
## ott7041853, mrcaott15654ott36347, mrcaott15654ott58342, mrcaott15654ott63026,
## mrcaott15654ott94650, mrcaott15654ott160687, mrcaott15654ott431661,
## mrcaott15654ott1069660, mrcaott15654ott617795, mrcaott15654ott35067,
## mrcaott15654ott277368, mrcaott15654ott16094, mrcaott15654ott35076,
## mrcaott15654ott15659, ott422680, ott5246131, ott147604, ott125642,
## mrcaott42ott658, ott947318, ott801601, ott278114, ott114656, ott458402,
## ott4940726, ott229562, ott229560, ott244265, ott229558, ott683263, ott392222,
## mrcaott42ott30082, ott392220, mrcaott42ott29157, ott864593, mrcaott42ott10477,
## mrcaott42ott38834, mrcaott42ott48903, mrcaott42ott254702, ott7067181, ott839752,
## mrcaott42ott45197, mrcaott42ott55942, mrcaott42ott102, mrcaott102ott739,
## ott816256, mrcaott102ott283439, mrcaott102ott8118, mrcaott102ott38119,
## mrcaott102ott125766, mrcaott102ott456651, mrcaott102ott1729, mrcaott102ott23039,
## mrcaott102ott289304, mrcaott102ott185328, mrcaott102ott542525,
## mrcaott102ott348560, mrcaott102ott542521, mrcaott102ott321218, ott392223,
## mrcaott1548ott4697, mrcaott4697ott263949, ott44565, mrcaott4697ott6940,
## ott827263, ott770319, mrcaott47497ott3612617, mrcaott47497ott3612529,
## mrcaott47497ott3612596, mrcaott47497ott3612516, mrcaott47497ott3612589,
## mrcaott47497ott3612591, mrcaott47497ott3612592, mrcaott47497ott77889,
## mrcaott47497ott110766, mrcaott47497ott3612579, mrcaott47497ott3612501,
## mrcaott47497ott3612503, mrcaott47497ott247331, mrcaott47497ott684074,
## mrcaott47497ott3612500, ott773483, ott285821, ott471203, ott212201, ott5506109,
## ott285819, ott5517919, mrcaott274ott595, ott1024043, mrcaott274ott3887,
## mrcaott3887ott9371, ott216171, ott739933, mrcaott3887ott28511, ott936925,
## mrcaott31485ott79094, mrcaott31485ott114152, ott739931, mrcaott45127ott730762,
## mrcaott45127ott135009, mrcaott45127ott57412, mrcaott57412ott361573, ott108736,
## ott108734, ott108720, mrcaott392ott44793, ott108719, mrcaott392ott63601,
## ott5551466, mrcaott392ott55142, mrcaott392ott7815, mrcaott392ott16164,
## mrcaott392ott3549, mrcaott3549ott17097, mrcaott3549ott7508, mrcaott3549ott5050,
## mrcaott3549ott6406, mrcaott3549ott143050, ott1032209, ott223661,
## mrcaott49ott6612, ott555379, ott257330, ott580673, ott1096612, ott3665427,
## ott1025977, ott335258, ott335259, ott910030, ott242963, ott576383, ott675306,
## ott1037807, ott987479, ott471706, ott5673589, ott251966, ott471705, ott492249,
## ott5673590, ott5677241, ott2942239, ott2942245, ott29723, ott750565, ott150273,
## ott176166, ott2844994, mrcaott# tree with resolved polytomies:
ResolvedPolytomiesTree<- multi2di(AllTree)
# write to files
write.tree(AllTree, file='data/phylogeny_all.txt') #phylogeny
write.tree(AllTree, file='data/phylogeny_all_res_polytomy.txt') #phylogeny with resolved polytomies
write.csv(ResolvedNames, 'data/phylogeny_species_names.csv') #list of names
write.csv(ResolvedNamesInTree, 'data/phylogeny_species_names_in_tree.csv') #list of names that are present in the treePreparing the phylogeny data
#read in phylogeny
phylo<- ape::read.tree('data/phylogeny_all.txt')
#subset df above with just those that are in the tree (note some missing)
names_in_tree<- read.csv('data/phylogeny_species_names_in_tree.csv')
names_in_tree$ott_id<- paste('ott',names_in_tree$ott_id, sep = '')
#phylo$tip.label %in% names_in_tree$ott_id #to check whether they're all present
names_in_tree<- subset(names_in_tree, names_in_tree$ott_id %in% phylo$tip.label)
#which species in the table are in the tree
df$species.updated.rotl<- tolower(df$species.updated.rotl)
df<- subset(df, tolower(df$species.updated.rotl) %in% tolower(names_in_tree$search_string))
#give them the ids in a column sot hat brms can match it up
df$species_id<- names_in_tree$ott_idCovariance matrix produced using branch lengths of 1 (as in Fisher et al)
# set branch lengths to 1 for covariance matrix
phylo_1b <- compute.brlen(phylo, 1)
#create covariance matrix
CovarMatrix <- ape::vcv.phylo(phylo_1b)#plot phylogeny to check
plot(AllTree, no.margin = TRUE, cex = 0.5, label.offset = 0.5)
Simple phylogenetic tree constructed using the Open Tree of Life, needs manually checked for errors
data tidying
Change the fission or budding observed column to ‘yes’ vs ‘no’ to make sure it is not doing some weird numeric thing
df$FissionOrBuddingObserved_Genus_nominal <- ifelse(df$FissionOrBuddingObserved_Genus == 1, 'yes','no')early = c('1','1,2','2')
df$germline_timing_simple<- ifelse(df$germline_timing %in% early, 'early', df$germline_timing)
df$germline_timing_simple<- ifelse(df$germline_timing == '0', 'no_germline', df$germline_timing_simple)
df$germline_timing_simple<- ifelse(df$germline_timing == '3', 'adult', df$germline_timing_simple) Analyses
Conducting Bayesian analyses using BRMS, first without phylogeny included, then including phylogeny. Each analysis contains the code that defines the model, and briefly analyses them– producing summary stats and figs.
See below for analyses of:
Each analysis uses vague priors, 5 chains, 6,000,000 iterations, 100000 of which are discarded as warm-up, thinned by a factor of 1000.
(Currently use reproductive traits of genus rather than species)
- Phylogenetically naive:
- Does a single-celled bottleneck correlate with increased cell number? (priors = normal dist, mean of 0, sd of 10)
- Does a single-celled bottleneck correlate with increased cell types (per cell)? prior(normal(0, 10), “b”)
- Does germline timing correlate with increased cell number? prior = prior(normal(0, 10), “b”)
- Does germline timing correlate with increased cell types (per cell)?
- Phylogenetically informed:
- Does a single-celled bottleneck correlate with increased cell number?
- Does a single-celled bottleneck correlate with increased cell types (per cell)?
- Does germline timing correlate with increased cell number?
- Does germline timing correlate with increased cell types (per cell)?
- Does a single-celled bottleneck correlate with increased cell number?
To do: do any of these things need scaled?
Phylogenetically naive
Does a single-celled bottleneck correlate with increased cell number?
Using simple normally-distributed, relatively non-informative prior
One possibility is that the 0s and 1s are treated as numeric, when they should be categorical, so change to yes/no.
#fit the model
fit_fission_cell_num<-
brm(data = df,
family=gaussian(), # family of model
formula = log(cell_number) ~ 0 + FissionOrBuddingObserved_Genus_nominal, #formula, the 0 means that there are estimates for both clonal and non-clonal, rather than relative to each other
iter = 6000000, warmup = 100000, chains = 5,thin = 1000, cores = 5, #chain settings
prior = prior(normal(0, 10), "b"), #defining the priors- for things in 'class b' set this prior. (can also use get_priors() and set_priors() fucncitons)
file = 'fits/fit_FissionCellNumber' )
#### Assessing model:
plot(fit_fission_cell_num) #check that chains converged
pp_check(fit_fission_cell_num) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
summary(fit_fission_cell_num) #summary of model ## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: log(cell_number) ~ 0 + FissionOrBuddingObserved_Genus_nominal
## Data: df (Number of observations: 155)
## Samples: 5 chains, each with iter = 6e+06; warmup = 1e+05; thin = 1000;
## total post-warmup samples = 29500
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## FissionOrBuddingObserved_Genus_nominalno 12.41 0.88 10.66 14.11
## FissionOrBuddingObserved_Genus_nominalyes 15.88 1.03 13.86 17.89
## Rhat Bulk_ESS Tail_ESS
## FissionOrBuddingObserved_Genus_nominalno 1.00 29496 29547
## FissionOrBuddingObserved_Genus_nominalyes 1.00 29056 28985
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 8.38 0.48 7.50 9.40 1.00 29919 29801
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).posterior_summary(fit_fission_cell_num, robust = T)## Estimate Est.Error Q2.5
## b_FissionOrBuddingObserved_Genus_nominalno 12.418883 0.8870628 10.658512
## b_FissionOrBuddingObserved_Genus_nominalyes 15.879885 1.0370737 13.863384
## sigma 8.356744 0.4742410 7.501903
## lp__ -558.203857 1.0075698 -561.736062
## Q97.5
## b_FissionOrBuddingObserved_Genus_nominalno 14.106579
## b_FissionOrBuddingObserved_Genus_nominalyes 17.891522
## sigma 9.403149
## lp__ -557.106225plot(conditional_effects(fit_fission_cell_num, points = TRUE, ask = F)) 
hyp = hypothesis(fit_fission_cell_num, "FissionOrBuddingObserved_Genus_nominalno > FissionOrBuddingObserved_Genus_nominalyes") #test hypothesis that there is no difference based on coefficients
hyp## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (FissionOrBudding... > 0 -3.47 1.36 -5.72 -1.24 0.01
## Post.Prob Star
## 1 0.01
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(hyp)
Does a single-celled bottleneck correlate with increased cell types (per cell)?
#fit the model
fit_fission_cell_type<-
brm(data = df,
family=poisson(),
formula = cell_types ~ 0 + FissionOrBuddingObserved_Genus_nominal + scale(log(cell_number)), #formula, the 0 means that there are estimates for both clonal and non-clonal, rather than relative to each other
iter = 6000000, warmup = 100000, chains = 5,thin = 1000, cores = 5, #chain settings
prior = prior(normal(0, 10), "b"), #defining the priors- for things in 'class b' set this prior. (can also use get_priors() and set_priors() fucncitons)
file = 'fits/fit_FissionCellType' )
plot(fit_fission_cell_type) #check that chains converged
pp_check(fit_fission_cell_type) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
summary(fit_fission_cell_type) #summary of model ## Family: poisson
## Links: mu = log
## Formula: cell_types ~ 0 + FissionOrBuddingObserved_Genus_nominal + scale(log(cell_number))
## Data: df (Number of observations: 154)
## Samples: 5 chains, each with iter = 6e+06; warmup = 1e+05; thin = 1000;
## total post-warmup samples = 29500
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## FissionOrBuddingObserved_Genus_nominalno 2.38 0.03 2.31 2.44
## FissionOrBuddingObserved_Genus_nominalyes 2.07 0.04 1.99 2.15
## scalelogcell_number 0.79 0.03 0.74 0.84
## Rhat Bulk_ESS Tail_ESS
## FissionOrBuddingObserved_Genus_nominalno 1.00 29863 28122
## FissionOrBuddingObserved_Genus_nominalyes 1.00 29051 29217
## scalelogcell_number 1.00 29251 29053
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).posterior_summary(fit_fission_cell_type, robust = T)## Estimate Est.Error
## b_FissionOrBuddingObserved_Genus_nominalno 2.3787467 0.03347560
## b_FissionOrBuddingObserved_Genus_nominalyes 2.0677443 0.04129789
## b_scalelogcell_number 0.7889378 0.02508357
## lp__ -1062.9584721 0.98458986
## Q2.5 Q97.5
## b_FissionOrBuddingObserved_Genus_nominalno 2.3124781 2.4435956
## b_FissionOrBuddingObserved_Genus_nominalyes 1.9861762 2.1479040
## b_scalelogcell_number 0.7397155 0.8380418
## lp__ -1066.4653841 -1061.8842775plot(conditional_effects(fit_fission_cell_type, points = TRUE, ask = F)) 
hyp = hypothesis(fit_fission_cell_type, "FissionOrBuddingObserved_Genus_nominalno > FissionOrBuddingObserved_Genus_nominalyes") #test hypothesis that there is no difference based on coefficients
hyp## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (FissionOrBudding... > 0 0.31 0.05 0.24 0.39 Inf
## Post.Prob Star
## 1 1 *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(hyp)
### Does germline timing correlate with increased cell number?
prior <- get_prior(log(cell_number) ~ 0 + germline_timing_simple, family=gaussian(), data = df) #what priors do we need to define?## Warning: Rows containing NAs were excluded from the model.#fit the model
fit_germline_cell_num<-
brm(data = df,
family=gaussian(), # family of model
formula = log(cell_number) ~ 0 + germline_timing_simple, #formula, the 0 means that there are estimates for both clonal and non-clonal, rather than relative to each other
iter = 6000000, warmup = 100000, chains = 5,thin = 1000, cores = 5, #chain settings
prior = prior(normal(0, 10), "b"), #defining the priors- for things in 'class b' set this prior. (can also use get_priors() and set_priors() fucncitons)
file = 'fits/fit_GermCellNum' )
plot(fit_germline_cell_num) #check that chains converged
pp_check(fit_germline_cell_num) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
plot(conditional_effects(fit_germline_cell_num), points = TRUE) 
summary(fit_germline_cell_num)## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: log(cell_number) ~ 0 + germline_timing_simple
## Data: df (Number of observations: 158)
## Samples: 5 chains, each with iter = 6e+06; warmup = 1e+05; thin = 1000;
## total post-warmup samples = 29500
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## germline_timing_simple 5.75 2.38 1.06 10.36 1.00
## germline_timing_simpleadult 16.80 0.70 15.42 18.18 1.00
## germline_timing_simpleearly 13.67 1.32 11.09 16.24 1.00
## germline_timing_simpleno_germline 3.15 1.45 0.34 5.98 1.00
## Bulk_ESS Tail_ESS
## germline_timing_simple 29223 28981
## germline_timing_simpleadult 28851 27031
## germline_timing_simpleearly 28632 28205
## germline_timing_simpleno_germline 29223 29179
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 6.96 0.40 6.23 7.80 1.00 29352 29552
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).pairs(fit_germline_cell_num)
posterior_summary(fit_germline_cell_num, robust = T) #what are the medians for coefficients? if F, then returns means## Estimate Est.Error Q2.5
## b_germline_timing_simple 5.740959 2.3853286 1.0647546
## b_germline_timing_simpleadult 16.795643 0.6978221 15.4247193
## b_germline_timing_simpleearly 13.658220 1.3175933 11.0893763
## b_germline_timing_simpleno_germline 3.150338 1.4499312 0.3424271
## sigma 6.938587 0.3919637 6.2309749
## lp__ -546.467963 1.4160427 -550.7999339
## Q97.5
## b_germline_timing_simple 10.358601
## b_germline_timing_simpleadult 18.178305
## b_germline_timing_simpleearly 16.244379
## b_germline_timing_simpleno_germline 5.982424
## sigma 7.798130
## lp__ -544.704614Does germline timing correlate with increased cell types (per cell)?
fit_type<-
brm(data = df,
family=poisson(),
formula = cell_types ~ 0 + germline_timing_simple + scale(log(cell_number)),
iter = 6000000, warmup = 100000, chains = 5, thin = 10000, cores = 5,
prior = prior(normal(0, 10), "b"), file = 'fits/fit_GermCellType')
plot(fit_type) #check that chains converged
pp_check(fit_type) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
summary(fit_type) #summary of model ## Family: poisson
## Links: mu = log
## Formula: cell_types ~ 0 + germline_timing_simple + scale(log(cell_number))
## Data: df (Number of observations: 157)
## Samples: 5 chains, each with iter = 6e+06; warmup = 1e+05; thin = 10000;
## total post-warmup samples = 2950
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## germline_timing_simple 2.55 0.14 2.26 2.82 1.00
## germline_timing_simpleadult 1.89 0.04 1.81 1.96 1.00
## germline_timing_simpleearly 3.21 0.04 3.13 3.28 1.00
## germline_timing_simpleno_germline 1.04 0.20 0.62 1.42 1.00
## scalelogcell_number 0.74 0.03 0.70 0.80 1.00
## Bulk_ESS Tail_ESS
## germline_timing_simple 2673 2593
## germline_timing_simpleadult 2830 2381
## germline_timing_simpleearly 2804 2903
## germline_timing_simpleno_germline 3223 2797
## scalelogcell_number 2872 2968
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).posterior_summary(fit_type, robust = T)## Estimate Est.Error Q2.5
## b_germline_timing_simple 2.5487216 0.14423226 2.2551736
## b_germline_timing_simpleadult 1.8894040 0.03775240 1.8144627
## b_germline_timing_simpleearly 3.2075050 0.03998024 3.1297714
## b_germline_timing_simpleno_germline 1.0396894 0.20202034 0.6211784
## b_scalelogcell_number 0.7445076 0.02645249 0.6956847
## lp__ -691.2963797 1.44798205 -695.5565536
## Q97.5
## b_germline_timing_simple 2.8158235
## b_germline_timing_simpleadult 1.9622219
## b_germline_timing_simpleearly 3.2847058
## b_germline_timing_simpleno_germline 1.4226097
## b_scalelogcell_number 0.7974566
## lp__ -689.5067283plot(conditional_effects(fit_type), points = TRUE, ask = F) 
Phylogenetically informed:
Does a single-celled bottleneck correlate with increased cell number?
fit_cellnumber_fission_phy<-
brm(data = df,
family=gaussian(), # family of model
formula = log(cell_number) ~ 0 + FissionOrBuddingObserved_Genus_nominal + (1|gr(species_id, cov = CovarMatrix)),
iter = 6000000, warmup = 100000, chains = 5, thin = 1000, cores = 5,
prior = prior(normal(0, 10), "b"), file = 'fits/fit_phy_FissionCellNumber', # same simple prior
data2 = list(CovarMatrix = CovarMatrix))
plot(fit_cellnumber_fission_phy) #check that chains converged
pp_check(fit_cellnumber_fission_phy) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
summary(fit_cellnumber_fission_phy) #summary of model ## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be
## careful when analysing the results! We recommend running more iterations and/or
## setting stronger priors.## Warning: There were 12874 divergent transitions after warmup.
## Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/
## warnings.html#divergent-transitions-after-warmup## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: log(cell_number) ~ 0 + FissionOrBuddingObserved_Genus_nominal + (1 | gr(species_id, cov = CovarMatrix))
## Data: df (Number of observations: 155)
## Samples: 5 chains, each with iter = 6e+06; warmup = 1e+05; thin = 1000;
## total post-warmup samples = 29500
##
## Group-Level Effects:
## ~species_id (Number of levels: 155)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.22 0.22 2.80 3.63 1.04 76 2143
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## FissionOrBuddingObserved_Genus_nominalno 7.00 3.07 0.62 13.20
## FissionOrBuddingObserved_Genus_nominalyes 5.51 2.99 -0.68 11.64
## Rhat Bulk_ESS Tail_ESS
## FissionOrBuddingObserved_Genus_nominalno 1.06 526 1236
## FissionOrBuddingObserved_Genus_nominalyes 1.03 730 1621
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 0.86 0.57 0.15 2.18 1.03 95 64
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).posterior_summary(fit_cellnumber_fission_phy, robust = T)## Estimate Est.Error
## b_FissionOrBuddingObserved_Genus_nominalno 7.06462590 2.5895889
## b_FissionOrBuddingObserved_Genus_nominalyes 5.45522328 2.6092922
## sd_species_id__Intercept 3.20384042 0.2264722
## sigma 0.72089274 0.5919395
## r_species_id[ott1002450,Intercept] -1.85243086 2.8090915
## r_species_id[ott1017821,Intercept] -2.80044951 2.7038441
## r_species_id[ott1052546,Intercept] -2.73875225 2.7642125
## r_species_id[ott1059898,Intercept] 4.01304318 2.7785176
## r_species_id[ott1059900,Intercept] 5.82412830 2.7165660
## r_species_id[ott1061937,Intercept] -0.14428369 2.6766553
## r_species_id[ott1069171,Intercept] 14.48010103 2.7532761
## r_species_id[ott1072227,Intercept] -4.24083957 2.6928973
## r_species_id[ott108923,Intercept] 7.71513917 2.6102208
## r_species_id[ott1099013,Intercept] 16.80643889 2.6667915
## r_species_id[ott111442,Intercept] 5.52938040 2.6811066
## r_species_id[ott112015,Intercept] -4.17709194 2.6744641
## r_species_id[ott112016,Intercept] -4.20107568 2.7372812
## r_species_id[ott112017,Intercept] -4.20765377 2.7074165
## r_species_id[ott127047,Intercept] -0.35580876 2.7180326
## r_species_id[ott150272,Intercept] 1.86807718 2.6927313
## r_species_id[ott160850,Intercept] 4.39747634 2.7447592
## r_species_id[ott165368,Intercept] 18.85790714 2.7583775
## r_species_id[ott167121,Intercept] -3.50129469 2.7580639
## r_species_id[ott178177,Intercept] 12.69432336 2.6601421
## r_species_id[ott178412,Intercept] 23.66802988 2.9719074
## r_species_id[ott181933,Intercept] 20.90184100 2.7906664
## r_species_id[ott182906,Intercept] 18.99877082 2.8370405
## r_species_id[ott186999,Intercept] 16.99113276 2.7565517
## r_species_id[ott187583,Intercept] 1.26693806 2.6848839
## r_species_id[ott199292,Intercept] 9.58302637 2.7507597
## r_species_id[ott207134,Intercept] 14.86303398 2.7465218
## r_species_id[ott215125,Intercept] 18.44067947 2.7236789
## r_species_id[ott216694,Intercept] 20.55006858 2.7228557
## r_species_id[ott223669,Intercept] 19.12623089 2.6900868
## r_species_id[ott225275,Intercept] 15.90837936 2.6453660
## r_species_id[ott237608,Intercept] 15.14576471 2.7744054
## r_species_id[ott246046,Intercept] 4.10050148 2.8871997
## r_species_id[ott247341,Intercept] 24.15825759 2.7530895
## r_species_id[ott256062,Intercept] -2.17294819 2.8040605
## r_species_id[ott256089,Intercept] -3.52491631 2.8130362
## r_species_id[ott256145,Intercept] -2.08522229 2.6759763
## r_species_id[ott263960,Intercept] 14.89675567 2.7572527
## r_species_id[ott263980,Intercept] 14.88774825 2.7323777
## r_species_id[ott263987,Intercept] 11.39585914 2.6754832
## r_species_id[ott263988,Intercept] 17.21976655 2.6710946
## r_species_id[ott265121,Intercept] 20.27596373 2.7982612
## r_species_id[ott266342,Intercept] 0.32072246 2.6698828
## r_species_id[ott269063,Intercept] -2.32046776 2.7214678
## r_species_id[ott275893,Intercept] 14.66677246 2.7158486
## r_species_id[ott275897,Intercept] 13.43791522 2.8018966
## r_species_id[ott2810724,Intercept] -3.07024715 2.7981436
## r_species_id[ott2819986,Intercept] 17.07850384 2.7711695
## r_species_id[ott2821097,Intercept] 15.45500911 2.6395185
## r_species_id[ott2844172,Intercept] 2.83086212 2.8482366
## r_species_id[ott2844962,Intercept] 0.25547343 2.7371942
## r_species_id[ott2849837,Intercept] -0.05825265 2.7480528
## r_species_id[ott2942244,Intercept] 4.70885092 2.7197860
## r_species_id[ott316441,Intercept] 15.59252344 2.7838582
## r_species_id[ott33153,Intercept] -2.68853508 2.6941110
## r_species_id[ott336388,Intercept] 20.17661393 2.7708653
## r_species_id[ott34559,Intercept] 15.96944183 2.7292542
## r_species_id[ott346740,Intercept] 19.16350509 2.7894092
## r_species_id[ott3583594,Intercept] 2.11305880 2.7593074
## r_species_id[ott3587677,Intercept] -0.68530482 2.7407637
## r_species_id[ott359012,Intercept] -4.26750563 2.7914553
## r_species_id[ott361837,Intercept] -2.64190158 2.6623175
## r_species_id[ott362913,Intercept] 5.03144730 2.7262401
## r_species_id[ott365439,Intercept] 3.27566767 2.6616060
## r_species_id[ott3663378,Intercept] 11.54967001 2.7989450
## r_species_id[ott3665433,Intercept] 5.69653135 2.7113222
## r_species_id[ott3684291,Intercept] 0.22115732 2.6739226
## r_species_id[ott3684365,Intercept] 0.30961312 2.6899387
## r_species_id[ott3684379,Intercept] -3.92942596 2.8074330
## r_species_id[ott3684389,Intercept] -1.64064479 2.6989463
## r_species_id[ott3684437,Intercept] -3.57846033 2.8029167
## r_species_id[ott381979,Intercept] -3.89687613 2.7058268
## r_species_id[ott381980,Intercept] 3.14747236 2.6965249
## r_species_id[ott381983,Intercept] -4.48010146 2.7381493
## r_species_id[ott395048,Intercept] -0.43657552 2.7761680
## r_species_id[ott3974169,Intercept] 17.80063262 2.7558329
## r_species_id[ott3995126,Intercept] 18.77244271 2.6970873
## r_species_id[ott4010019,Intercept] 16.82535782 2.7518213
## r_species_id[ott4010960,Intercept] 19.10451446 2.7397183
## r_species_id[ott4011155,Intercept] 10.62806401 2.9390731
## r_species_id[ott4013437,Intercept] 16.19825677 2.7242763
## r_species_id[ott4013674,Intercept] 20.35575993 2.8604399
## r_species_id[ott4013684,Intercept] 19.11799438 2.8062905
## r_species_id[ott422679,Intercept] 0.31217461 2.6767994
## r_species_id[ott431388,Intercept] 8.32818209 2.6443083
## r_species_id[ott446088,Intercept] 3.66570294 2.6914361
## r_species_id[ott4741377,Intercept] 15.38966560 2.6838422
## r_species_id[ott4742064,Intercept] 20.14242643 2.8238228
## r_species_id[ott481952,Intercept] 19.43833164 2.8227131
## r_species_id[ott48288,Intercept] 14.85349649 2.7291567
## r_species_id[ott485470,Intercept] 0.44638186 2.6846161
## r_species_id[ott485473,Intercept] -2.06078688 2.6968523
## r_species_id[ott485476,Intercept] -3.49008996 2.7191755
## r_species_id[ott485480,Intercept] 2.56903733 2.7001135
## r_species_id[ott485482,Intercept] 0.26986461 2.7084729
## r_species_id[ott486834,Intercept] -0.74023063 2.7435296
## r_species_id[ott490206,Intercept] 6.01146782 2.7661244
## r_species_id[ott492241,Intercept] 0.93177169 2.7597267
## r_species_id[ott497063,Intercept] 15.07278049 2.7461882
## r_species_id[ott4974308,Intercept] 5.88885260 2.8797881
## r_species_id[ott4978773,Intercept] 1.16175026 2.7343386
## r_species_id[ott4979583,Intercept] 1.83138040 2.7005108
## r_species_id[ott518643,Intercept] 3.59204363 2.6888601
## r_species_id[ott542509,Intercept] 18.94816328 2.7020345
## r_species_id[ott54768,Intercept] 9.59079817 2.7571210
## r_species_id[ott549846,Intercept] 7.06127581 2.7288303
## r_species_id[ott560703,Intercept] -1.97294064 2.7702370
## r_species_id[ott567703,Intercept] 18.06534720 2.7087620
## r_species_id[ott570365,Intercept] 0.60554246 2.7851363
## r_species_id[ott570656,Intercept] 12.09267016 2.7319978
## r_species_id[ott588761,Intercept] 3.69688440 2.7379335
## r_species_id[ott592355,Intercept] 14.97983887 2.7412680
## r_species_id[ott601255,Intercept] 23.16553691 2.7210835
## r_species_id[ott602180,Intercept] -0.84721264 2.7104600
## r_species_id[ott60470,Intercept] -2.67504695 2.7610017
## r_species_id[ott60473,Intercept] -2.70017893 2.7153491
## r_species_id[ott60479,Intercept] -3.48652780 2.7023470
## r_species_id[ott633708,Intercept] 1.12622173 2.6814608
## r_species_id[ott633710,Intercept] 3.15557170 2.7344595
## r_species_id[ott633711,Intercept] 1.76278892 2.7059625
## r_species_id[ott633717,Intercept] -2.26409405 2.7441659
## r_species_id[ott633719,Intercept] 2.42368014 2.7063111
## r_species_id[ott643237,Intercept] 16.69984964 2.7296852
## r_species_id[ott645555,Intercept] 19.63684711 2.7968853
## r_species_id[ott649193,Intercept] 18.55450035 2.7795635
## r_species_id[ott675301,Intercept] 1.11082937 2.7405402
## r_species_id[ott724784,Intercept] 10.33238337 2.9094993
## r_species_id[ott72522,Intercept] 20.29659929 2.6985701
## r_species_id[ott727979,Intercept] 21.71416569 2.8690236
## r_species_id[ott733462,Intercept] 16.89958936 2.7111354
## r_species_id[ott736728,Intercept] 5.37033166 2.7363974
## r_species_id[ott742128,Intercept] 3.56133377 2.6983100
## r_species_id[ott7489702,Intercept] 3.80584590 2.7126916
## r_species_id[ott7567530,Intercept] 5.49684986 2.7330045
## r_species_id[ott765113,Intercept] -0.52866604 2.7085757
## r_species_id[ott765280,Intercept] 13.86883792 2.6744774
## r_species_id[ott779028,Intercept] 16.42257505 2.7051891
## r_species_id[ott790395,Intercept] 15.99967293 2.6977480
## r_species_id[ott817791,Intercept] 14.61663453 2.8390008
## r_species_id[ott821356,Intercept] 15.18999811 2.8275455
## r_species_id[ott83430,Intercept] 11.33357986 2.6885018
## r_species_id[ott83432,Intercept] 5.11148068 2.8690091
## r_species_id[ott840001,Intercept] 18.43516581 2.7722402
## r_species_id[ott841027,Intercept] 0.29718964 2.7355523
## r_species_id[ott849781,Intercept] 13.98145426 2.7217247
## r_species_id[ott878345,Intercept] 5.76361294 2.7333749
## r_species_id[ott92556,Intercept] 13.10896349 2.7573013
## r_species_id[ott92561,Intercept] 14.58847774 2.6771453
## r_species_id[ott939432,Intercept] 4.38779929 2.6871800
## r_species_id[ott939454,Intercept] 4.42192810 2.6682235
## r_species_id[ott954042,Intercept] 16.28009470 2.8028405
## r_species_id[ott958293,Intercept] -3.35840565 2.7726895
## r_species_id[ott958304,Intercept] -4.24099051 2.7461189
## r_species_id[ott962359,Intercept] 2.68090529 2.7190010
## r_species_id[ott987480,Intercept] 0.19368309 2.7132913
## lp__ -399.71144620 137.6455875
## Q2.5 Q97.5
## b_FissionOrBuddingObserved_Genus_nominalno 0.6153936 13.195213
## b_FissionOrBuddingObserved_Genus_nominalyes -0.6794411 11.638574
## sd_species_id__Intercept 2.8047455 3.630059
## sigma 0.1534412 2.183785
## r_species_id[ott1002450,Intercept] -8.2535254 4.507410
## r_species_id[ott1017821,Intercept] -9.2129083 3.850850
## r_species_id[ott1052546,Intercept] -9.2473592 3.854425
## r_species_id[ott1059898,Intercept] -2.3087431 10.518682
## r_species_id[ott1059900,Intercept] -0.5831382 12.294295
## r_species_id[ott1061937,Intercept] -6.4365593 6.695234
## r_species_id[ott1069171,Intercept] 7.8528621 20.883961
## r_species_id[ott1072227,Intercept] -10.6696703 2.511261
## r_species_id[ott108923,Intercept] 1.3654064 14.159741
## r_species_id[ott1099013,Intercept] 10.2768144 23.359566
## r_species_id[ott111442,Intercept] -0.9207088 11.972166
## r_species_id[ott112015,Intercept] -10.6606933 2.386327
## r_species_id[ott112016,Intercept] -10.6022482 2.363083
## r_species_id[ott112017,Intercept] -10.6540915 2.398650
## r_species_id[ott127047,Intercept] -6.5244252 6.450267
## r_species_id[ott150272,Intercept] -4.6891239 8.410393
## r_species_id[ott160850,Intercept] -2.1814263 10.927224
## r_species_id[ott165368,Intercept] 12.6696491 25.828349
## r_species_id[ott167121,Intercept] -9.8557888 3.145288
## r_species_id[ott178177,Intercept] 6.4304450 19.300072
## r_species_id[ott178412,Intercept] 16.4810238 30.084451
## r_species_id[ott181933,Intercept] 14.4299745 27.617541
## r_species_id[ott182906,Intercept] 12.1287514 25.699551
## r_species_id[ott186999,Intercept] 10.5078434 23.372559
## r_species_id[ott187583,Intercept] -5.2000029 7.937452
## r_species_id[ott199292,Intercept] 3.1507855 16.023225
## r_species_id[ott207134,Intercept] 8.2895121 21.492542
## r_species_id[ott215125,Intercept] 12.0046116 24.849939
## r_species_id[ott216694,Intercept] 14.0932150 27.172168
## r_species_id[ott223669,Intercept] 12.5827142 25.795217
## r_species_id[ott225275,Intercept] 9.4446519 22.573019
## r_species_id[ott237608,Intercept] 8.4970227 21.482666
## r_species_id[ott246046,Intercept] -2.3948808 10.454370
## r_species_id[ott247341,Intercept] 17.3672435 30.799265
## r_species_id[ott256062,Intercept] -8.5688555 4.555120
## r_species_id[ott256089,Intercept] -9.6511326 3.491819
## r_species_id[ott256145,Intercept] -8.5432216 4.306427
## r_species_id[ott263960,Intercept] 8.3898498 21.332512
## r_species_id[ott263980,Intercept] 8.3715337 21.307315
## r_species_id[ott263987,Intercept] 4.9957440 17.854514
## r_species_id[ott263988,Intercept] 10.6184854 23.805139
## r_species_id[ott265121,Intercept] 13.5209187 26.770808
## r_species_id[ott266342,Intercept] -5.8356101 7.291808
## r_species_id[ott269063,Intercept] -8.7328513 4.116569
## r_species_id[ott275893,Intercept] 8.1221434 21.042547
## r_species_id[ott275897,Intercept] 6.9637121 20.058729
## r_species_id[ott2810724,Intercept] -9.0700272 4.110758
## r_species_id[ott2819986,Intercept] 10.5575491 23.722539
## r_species_id[ott2821097,Intercept] 9.2464122 22.282312
## r_species_id[ott2844172,Intercept] -3.5657695 9.224794
## r_species_id[ott2844962,Intercept] -6.1645262 6.854245
## r_species_id[ott2849837,Intercept] -6.2970802 6.858210
## r_species_id[ott2942244,Intercept] -1.7905999 11.357933
## r_species_id[ott316441,Intercept] 8.7586729 22.280565
## r_species_id[ott33153,Intercept] -9.1698201 3.965317
## r_species_id[ott336388,Intercept] 13.5516126 26.778124
## r_species_id[ott34559,Intercept] 9.3806520 22.584391
## r_species_id[ott346740,Intercept] 12.6480426 25.411975
## r_species_id[ott3583594,Intercept] -4.2737786 8.854982
## r_species_id[ott3587677,Intercept] -7.1759664 5.840956
## r_species_id[ott359012,Intercept] -10.5232298 2.467659
## r_species_id[ott361837,Intercept] -9.0906803 3.689092
## r_species_id[ott362913,Intercept] -1.4602325 11.359886
## r_species_id[ott365439,Intercept] -2.8674215 10.093817
## r_species_id[ott3663378,Intercept] 4.9880248 18.179411
## r_species_id[ott3665433,Intercept] -0.8267493 12.380952
## r_species_id[ott3684291,Intercept] -6.2346401 6.833449
## r_species_id[ott3684365,Intercept] -6.2959529 6.872882
## r_species_id[ott3684379,Intercept] -10.3242629 2.722197
## r_species_id[ott3684389,Intercept] -8.1871576 4.936396
## r_species_id[ott3684437,Intercept] -9.9204912 3.142227
## r_species_id[ott381979,Intercept] -10.3520114 2.561869
## r_species_id[ott381980,Intercept] -3.5255402 9.680731
## r_species_id[ott381983,Intercept] -10.7514549 2.465423
## r_species_id[ott395048,Intercept] -6.5306492 6.616935
## r_species_id[ott3974169,Intercept] 11.3670921 24.284927
## r_species_id[ott3995126,Intercept] 12.2511308 25.194752
## r_species_id[ott4010019,Intercept] 10.5800765 23.760267
## r_species_id[ott4010960,Intercept] 12.5779476 25.446885
## r_species_id[ott4011155,Intercept] 4.3694689 17.244428
## r_species_id[ott4013437,Intercept] 9.7964007 22.604851
## r_species_id[ott4013674,Intercept] 13.7536902 26.748760
## r_species_id[ott4013684,Intercept] 12.5436223 25.407325
## r_species_id[ott422679,Intercept] -6.0433408 6.705244
## r_species_id[ott431388,Intercept] 1.8792033 14.800622
## r_species_id[ott446088,Intercept] -2.7957409 10.121815
## r_species_id[ott4741377,Intercept] 8.9221781 21.722311
## r_species_id[ott4742064,Intercept] 13.5650631 26.515198
## r_species_id[ott481952,Intercept] 12.7069011 25.874878
## r_species_id[ott48288,Intercept] 8.2484201 21.429629
## r_species_id[ott485470,Intercept] -5.9854746 7.246224
## r_species_id[ott485473,Intercept] -8.5832512 4.606154
## r_species_id[ott485476,Intercept] -9.8571933 3.199908
## r_species_id[ott485480,Intercept] -3.9950507 9.210695
## r_species_id[ott485482,Intercept] -5.8149346 7.240549
## r_species_id[ott486834,Intercept] -7.1633583 5.949320
## r_species_id[ott490206,Intercept] -0.5466732 12.553794
## r_species_id[ott492241,Intercept] -5.2772837 7.837014
## r_species_id[ott497063,Intercept] 8.6915411 21.693613
## r_species_id[ott4974308,Intercept] -0.4828801 12.412550
## r_species_id[ott4978773,Intercept] -5.1480894 7.891810
## r_species_id[ott4979583,Intercept] -4.5609448 8.490329
## r_species_id[ott518643,Intercept] -2.8578090 10.251227
## r_species_id[ott542509,Intercept] 12.6055087 25.650458
## r_species_id[ott54768,Intercept] 3.0906864 15.982108
## r_species_id[ott549846,Intercept] 0.6143222 13.447608
## r_species_id[ott560703,Intercept] -8.3359703 4.813498
## r_species_id[ott567703,Intercept] 11.5668594 24.353932
## r_species_id[ott570365,Intercept] -5.7910680 7.145330
## r_species_id[ott570656,Intercept] 5.6397451 18.552881
## r_species_id[ott588761,Intercept] -2.7524379 10.092405
## r_species_id[ott592355,Intercept] 8.4001517 21.246589
## r_species_id[ott601255,Intercept] 16.6179137 29.604265
## r_species_id[ott602180,Intercept] -7.2758515 5.577403
## r_species_id[ott60470,Intercept] -9.1525329 3.786283
## r_species_id[ott60473,Intercept] -9.1425320 3.715511
## r_species_id[ott60479,Intercept] -9.8946869 3.060752
## r_species_id[ott633708,Intercept] -5.4465843 7.770535
## r_species_id[ott633710,Intercept] -3.5359003 9.750626
## r_species_id[ott633711,Intercept] -4.6078295 8.527661
## r_species_id[ott633717,Intercept] -8.6960403 4.483246
## r_species_id[ott633719,Intercept] -4.0054521 9.106923
## r_species_id[ott643237,Intercept] 10.5442387 23.569382
## r_species_id[ott645555,Intercept] 13.0318503 26.048557
## r_species_id[ott649193,Intercept] 12.0564825 24.905895
## r_species_id[ott675301,Intercept] -5.3667912 7.687026
## r_species_id[ott724784,Intercept] 3.9919737 16.818289
## r_species_id[ott72522,Intercept] 13.5598568 26.889265
## r_species_id[ott727979,Intercept] 15.2729611 28.061368
## r_species_id[ott733462,Intercept] 10.3682289 23.292054
## r_species_id[ott736728,Intercept] -0.6671030 12.526205
## r_species_id[ott742128,Intercept] -2.9410691 9.921909
## r_species_id[ott7489702,Intercept] -2.3130235 10.817222
## r_species_id[ott7567530,Intercept] -0.9381590 11.964005
## r_species_id[ott765113,Intercept] -6.6718029 6.330556
## r_species_id[ott765280,Intercept] 7.3496208 20.316973
## r_species_id[ott779028,Intercept] 9.9072820 22.780252
## r_species_id[ott790395,Intercept] 9.5008236 22.492745
## r_species_id[ott817791,Intercept] 8.1044548 21.230039
## r_species_id[ott821356,Intercept] 8.6940649 21.516381
## r_species_id[ott83430,Intercept] 5.3467771 18.370723
## r_species_id[ott83432,Intercept] -1.2166550 12.070073
## r_species_id[ott840001,Intercept] 11.8687001 24.716835
## r_species_id[ott841027,Intercept] -5.9138623 7.292688
## r_species_id[ott849781,Intercept] 7.7855769 20.916326
## r_species_id[ott878345,Intercept] -0.2970872 12.864811
## r_species_id[ott92556,Intercept] 6.5514766 19.478697
## r_species_id[ott92561,Intercept] 8.0554953 21.276904
## r_species_id[ott939432,Intercept] -2.0418734 10.982796
## r_species_id[ott939454,Intercept] -2.0257961 10.953848
## r_species_id[ott954042,Intercept] 9.4738346 22.910687
## r_species_id[ott958293,Intercept] -9.7746836 3.248121
## r_species_id[ott958304,Intercept] -10.6755199 2.547868
## r_species_id[ott962359,Intercept] -3.8244462 9.103583
## r_species_id[ott987480,Intercept] -5.9046678 7.219286
## lp__ -571.1418682 -175.711027plot(conditional_effects(fit_cellnumber_fission_phy, points = TRUE, ask = F)) 
hyp = hypothesis(fit_cellnumber_fission_phy, "FissionOrBuddingObserved_Genus_nominalno > FissionOrBuddingObserved_Genus_nominalyes") #test hypothesis that there is no difference based on coefficients
hyp## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (FissionOrBudding... > 0 1.48 0.95 -0.13 2.98 15.82
## Post.Prob Star
## 1 0.94
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(hyp)
### Does a single-celled bottleneck correlate with increased cell types (per cell)?
#fit the model
phylo_fit_fission_cell_type<-
brm(data = df,
family=poisson(),
formula = cell_types ~ 0 + FissionOrBuddingObserved_Genus_nominal + scale(log(cell_number)) + (1|gr(species_id, cov = CovarMatrix)), #formula, the 0 means that there are estimates for both clonal and non-clonal, rather than relative to each other
iter = 600000, warmup = 10000, chains = 5,thin = 100, cores = 5, #chain settings
prior = prior(normal(0, 10), "b"), #defining the priors- for things in 'class b' set this prior. (can also use get_priors() and set_priors() fucncitons)
data2 = list(CovarMatrix = CovarMatrix),
file = 'fits/fit_phy_FissionCellType')
plot(phylo_fit_fission_cell_type) #check that chains converged
pp_check(phylo_fit_fission_cell_type) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
summary(phylo_fit_fission_cell_type) #summary of model ## Family: poisson
## Links: mu = log
## Formula: cell_types ~ 0 + FissionOrBuddingObserved_Genus_nominal + scale(log(cell_number)) + (1 | gr(species_id, cov = CovarMatrix))
## Data: df (Number of observations: 154)
## Samples: 5 chains, each with iter = 6e+05; warmup = 10000; thin = 100;
## total post-warmup samples = 29500
##
## Group-Level Effects:
## ~species_id (Number of levels: 154)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.21 0.02 0.17 0.25 1.00 28860 28825
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI
## FissionOrBuddingObserved_Genus_nominalno 1.58 0.29 1.01 2.13
## FissionOrBuddingObserved_Genus_nominalyes 1.66 0.28 1.10 2.21
## scalelogcell_number 0.66 0.07 0.53 0.78
## Rhat Bulk_ESS Tail_ESS
## FissionOrBuddingObserved_Genus_nominalno 1.00 29830 28977
## FissionOrBuddingObserved_Genus_nominalyes 1.00 29526 29464
## scalelogcell_number 1.00 29588 28713
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).posterior_summary(phylo_fit_fission_cell_type, robust = T)## Estimate Est.Error
## b_FissionOrBuddingObserved_Genus_nominalno 1.57975901 0.28431764
## b_FissionOrBuddingObserved_Genus_nominalyes 1.66406774 0.28292158
## b_scalelogcell_number 0.65641396 0.06464607
## sd_species_id__Intercept 0.20796192 0.02185879
## r_species_id[ott1017821,Intercept] -0.13872134 0.41541290
## r_species_id[ott1052546,Intercept] -0.14202203 0.41569741
## r_species_id[ott1059898,Intercept] 0.67425543 0.37103178
## r_species_id[ott1059900,Intercept] 0.70214142 0.36523915
## r_species_id[ott1061937,Intercept] -0.47558323 0.44852263
## r_species_id[ott1069171,Intercept] 0.61846065 0.34245288
## r_species_id[ott1072227,Intercept] -0.51889050 0.42036713
## r_species_id[ott108923,Intercept] 0.75828047 0.35893339
## r_species_id[ott1099013,Intercept] -0.37700487 0.35691566
## r_species_id[ott111442,Intercept] 0.99426510 0.35452663
## r_species_id[ott112015,Intercept] -0.53998180 0.41456195
## r_species_id[ott112016,Intercept] -0.53559528 0.41585806
## r_species_id[ott112017,Intercept] -0.54191515 0.41019696
## r_species_id[ott127047,Intercept] 0.73698935 0.37072093
## r_species_id[ott150272,Intercept] 1.59267695 0.34025351
## r_species_id[ott160850,Intercept] -0.23383634 0.41276043
## r_species_id[ott165368,Intercept] 2.18739929 0.31785001
## r_species_id[ott167121,Intercept] -0.50458318 0.38241009
## r_species_id[ott178177,Intercept] 0.13150681 0.36757026
## r_species_id[ott178412,Intercept] 0.44073731 0.34472506
## r_species_id[ott181933,Intercept] -0.28722482 0.37756923
## r_species_id[ott182906,Intercept] 1.66822858 0.32320394
## r_species_id[ott186999,Intercept] 0.39693149 0.34835372
## r_species_id[ott187583,Intercept] -0.47546266 0.43292294
## r_species_id[ott199292,Intercept] 0.82750605 0.34826390
## r_species_id[ott207134,Intercept] -0.07822147 0.35192665
## r_species_id[ott215125,Intercept] 1.29486067 0.32792082
## r_species_id[ott216694,Intercept] -0.76164058 0.38449147
## r_species_id[ott223669,Intercept] 2.23114723 0.32006376
## r_species_id[ott225275,Intercept] 0.97837550 0.34025223
## r_species_id[ott237608,Intercept] -0.12069755 0.37577865
## r_species_id[ott246046,Intercept] 0.25976071 0.37444646
## r_species_id[ott247341,Intercept] 1.68170907 0.33499597
## r_species_id[ott256062,Intercept] -0.41285268 0.43137718
## r_species_id[ott256089,Intercept] -0.45039038 0.38337019
## r_species_id[ott256145,Intercept] -0.56779494 0.41047939
## r_species_id[ott263960,Intercept] 0.85936455 0.33689830
## r_species_id[ott263980,Intercept] -0.54663204 0.40019610
## r_species_id[ott263987,Intercept] -0.39599285 0.39271247
## r_species_id[ott263988,Intercept] -0.42067572 0.35497117
## r_species_id[ott265121,Intercept] -0.51478646 0.36474281
## r_species_id[ott266342,Intercept] -0.46656434 0.45433284
## r_species_id[ott269063,Intercept] -0.20507072 0.46026227
## r_species_id[ott275893,Intercept] -0.12956579 0.36874931
## r_species_id[ott275897,Intercept] -0.07735847 0.37338734
## r_species_id[ott2810724,Intercept] -0.20705787 0.38189415
## r_species_id[ott2819986,Intercept] -0.46214667 0.38472875
## r_species_id[ott2821097,Intercept] -0.32689895 0.35898502
## r_species_id[ott2844172,Intercept] 1.39569918 0.34114349
## r_species_id[ott2844962,Intercept] 1.56883984 0.34393042
## r_species_id[ott2849837,Intercept] 1.19147645 0.35662687
## r_species_id[ott2942244,Intercept] 1.35506835 0.34282721
## r_species_id[ott316441,Intercept] 1.62353900 0.32423215
## r_species_id[ott33153,Intercept] -0.35349988 0.39274277
## r_species_id[ott336388,Intercept] -0.47922532 0.37718759
## r_species_id[ott34559,Intercept] -0.42707040 0.37534701
## r_species_id[ott346740,Intercept] 0.82709450 0.34050393
## r_species_id[ott3583594,Intercept] 1.66052155 0.33289230
## r_species_id[ott3587677,Intercept] 2.03318476 0.33695204
## r_species_id[ott359012,Intercept] -0.38009492 0.45261320
## r_species_id[ott361837,Intercept] -0.56129288 0.41243900
## r_species_id[ott362913,Intercept] 1.16827850 0.34511698
## r_species_id[ott365439,Intercept] 1.09834027 0.34909657
## r_species_id[ott3663378,Intercept] 0.75893590 0.34125854
## r_species_id[ott3665433,Intercept] 1.12387845 0.34320195
## r_species_id[ott3684291,Intercept] 0.48095196 0.36815398
## r_species_id[ott3684365,Intercept] 0.40528632 0.38940367
## r_species_id[ott3684379,Intercept] 0.59824051 0.39931153
## r_species_id[ott3684389,Intercept] 0.45819897 0.39532629
## r_species_id[ott3684437,Intercept] 0.47846137 0.38283868
## r_species_id[ott381979,Intercept] -0.19405605 0.42198491
## r_species_id[ott381980,Intercept] -0.48817869 0.37758777
## r_species_id[ott381983,Intercept] -0.54190306 0.41251841
## r_species_id[ott395048,Intercept] 2.03160819 0.33225892
## r_species_id[ott3974169,Intercept] 1.16207700 0.33105235
## r_species_id[ott3995126,Intercept] 1.13136361 0.33115396
## r_species_id[ott4010019,Intercept] 0.06435637 0.34144404
## r_species_id[ott4010960,Intercept] 0.04211998 0.34661080
## r_species_id[ott4011155,Intercept] 0.23198067 0.34823376
## r_species_id[ott4013437,Intercept] 0.12690313 0.34562869
## r_species_id[ott4013674,Intercept] -0.04679345 0.34572949
## r_species_id[ott4013684,Intercept] 0.01571915 0.34514500
## r_species_id[ott422679,Intercept] -0.11248820 0.35550948
## r_species_id[ott431388,Intercept] 1.07355369 0.34666622
## r_species_id[ott446088,Intercept] 1.64480245 0.33427166
## r_species_id[ott4741377,Intercept] 0.09043462 0.34527037
## r_species_id[ott4742064,Intercept] 0.04106179 0.34469417
## r_species_id[ott481952,Intercept] 1.12896534 0.33378778
## r_species_id[ott48288,Intercept] 1.69545967 0.32308424
## r_species_id[ott485470,Intercept] -0.46987688 0.38023215
## r_species_id[ott485473,Intercept] -0.41487351 0.43365142
## r_species_id[ott485476,Intercept] -0.49827624 0.38018694
## r_species_id[ott485480,Intercept] -0.49558120 0.44330725
## r_species_id[ott485482,Intercept] -0.46853051 0.42576378
## r_species_id[ott486834,Intercept] -0.15409530 0.40042462
## r_species_id[ott490206,Intercept] 1.16390159 0.34553784
## r_species_id[ott492241,Intercept] 1.39004854 0.35131150
## r_species_id[ott497063,Intercept] -0.41083807 0.37993582
## r_species_id[ott4974308,Intercept] 1.23090854 0.33973088
## r_species_id[ott4978773,Intercept] 1.49034711 0.34912135
## r_species_id[ott4979583,Intercept] 1.42159116 0.34695325
## r_species_id[ott518643,Intercept] 1.22792497 0.34648331
## r_species_id[ott542509,Intercept] 2.04985414 0.32089922
## r_species_id[ott54768,Intercept] 0.77172239 0.35318013
## r_species_id[ott549846,Intercept] 1.08119581 0.34246706
## r_species_id[ott560703,Intercept] -0.03236044 0.40138249
## r_species_id[ott567703,Intercept] 0.04659771 0.34692188
## r_species_id[ott570365,Intercept] 0.74975762 0.36185658
## r_species_id[ott570656,Intercept] 0.91996686 0.34283062
## r_species_id[ott588761,Intercept] 1.08196305 0.34521115
## r_species_id[ott592355,Intercept] 0.44839413 0.34915002
## r_species_id[ott601255,Intercept] -0.81462935 0.39243299
## r_species_id[ott602180,Intercept] -0.22288764 0.43124467
## r_species_id[ott60470,Intercept] -0.56174080 0.40965967
## r_species_id[ott60473,Intercept] -0.56557852 0.40982512
## r_species_id[ott60479,Intercept] -0.50282785 0.38604557
## r_species_id[ott633708,Intercept] -0.44212085 0.42819965
## r_species_id[ott633710,Intercept] -0.48970492 0.37870794
## r_species_id[ott633711,Intercept] -0.48537809 0.42283802
## r_species_id[ott633717,Intercept] -0.39292927 0.44890892
## r_species_id[ott633719,Intercept] -0.48644088 0.37789982
## r_species_id[ott643237,Intercept] 1.26818632 0.33213476
## r_species_id[ott645555,Intercept] -0.21718373 0.35162902
## r_species_id[ott649193,Intercept] -0.71636829 0.38442834
## r_species_id[ott675301,Intercept] 1.41127832 0.34711307
## r_species_id[ott724784,Intercept] 0.03742364 0.35216406
## r_species_id[ott72522,Intercept] -0.21945725 0.36677039
## r_species_id[ott727979,Intercept] -0.81528057 0.39346925
## r_species_id[ott733462,Intercept] 0.28410783 0.35811379
## r_species_id[ott736728,Intercept] -0.06721470 0.38997597
## r_species_id[ott742128,Intercept] 0.86945655 0.33446909
## r_species_id[ott7489702,Intercept] 1.18302434 0.34464639
## r_species_id[ott7567530,Intercept] 0.72430636 0.36166701
## r_species_id[ott765113,Intercept] -0.06712116 0.34775022
## r_species_id[ott765280,Intercept] 0.81370689 0.34577741
## r_species_id[ott779028,Intercept] -0.05734221 0.34810196
## r_species_id[ott790395,Intercept] -0.42964802 0.35996587
## r_species_id[ott817791,Intercept] -0.29268480 0.37381063
## r_species_id[ott821356,Intercept] 0.95375638 0.34054801
## r_species_id[ott83430,Intercept] -0.35884674 0.36199917
## r_species_id[ott83432,Intercept] -0.35272549 0.36736051
## r_species_id[ott840001,Intercept] 1.23731547 0.33084455
## r_species_id[ott841027,Intercept] -0.46750432 0.45575787
## r_species_id[ott849781,Intercept] -0.51165616 0.39809053
## r_species_id[ott878345,Intercept] -0.37856252 0.40304440
## r_species_id[ott92556,Intercept] -0.14494907 0.35935992
## r_species_id[ott92561,Intercept] -0.21714118 0.35535410
## r_species_id[ott939432,Intercept] -0.37858169 0.36441454
## r_species_id[ott939454,Intercept] -0.37802167 0.36456947
## r_species_id[ott954042,Intercept] 1.02797506 0.33289498
## r_species_id[ott958293,Intercept] -0.47816028 0.40733579
## r_species_id[ott958304,Intercept] -0.51724078 0.41792039
## r_species_id[ott962359,Intercept] -0.25665223 0.41490378
## r_species_id[ott987480,Intercept] 1.47650113 0.34884478
## lp__ -576.07869834 10.96028520
## Q2.5 Q97.5
## b_FissionOrBuddingObserved_Genus_nominalno 1.00593684 2.12811496
## b_FissionOrBuddingObserved_Genus_nominalyes 1.09656422 2.20766975
## b_scalelogcell_number 0.52941160 0.78328728
## sd_species_id__Intercept 0.16896334 0.25484987
## r_species_id[ott1017821,Intercept] -0.96491904 0.66523797
## r_species_id[ott1052546,Intercept] -0.96401149 0.66054614
## r_species_id[ott1059898,Intercept] -0.07057473 1.39496130
## r_species_id[ott1059900,Intercept] -0.02353618 1.41131097
## r_species_id[ott1061937,Intercept] -1.36512534 0.39870404
## r_species_id[ott1069171,Intercept] -0.06208589 1.30017600
## r_species_id[ott1072227,Intercept] -1.36770676 0.29679213
## r_species_id[ott108923,Intercept] 0.06193942 1.46428249
## r_species_id[ott1099013,Intercept] -1.08219914 0.34952338
## r_species_id[ott111442,Intercept] 0.29624853 1.71099006
## r_species_id[ott112015,Intercept] -1.36447074 0.28110670
## r_species_id[ott112016,Intercept] -1.36275516 0.27851928
## r_species_id[ott112017,Intercept] -1.36269247 0.27002006
## r_species_id[ott127047,Intercept] -0.00374193 1.47065231
## r_species_id[ott150272,Intercept] 0.91961341 2.27592157
## r_species_id[ott160850,Intercept] -1.06589350 0.55791183
## r_species_id[ott165368,Intercept] 1.57585410 2.83028604
## r_species_id[ott167121,Intercept] -1.25889761 0.26203828
## r_species_id[ott178177,Intercept] -0.62078921 0.84807620
## r_species_id[ott178412,Intercept] -0.23059855 1.11670327
## r_species_id[ott181933,Intercept] -1.02322305 0.46476336
## r_species_id[ott182906,Intercept] 1.04135350 2.31710775
## r_species_id[ott186999,Intercept] -0.29549303 1.08993651
## r_species_id[ott187583,Intercept] -1.34318049 0.38374112
## r_species_id[ott199292,Intercept] 0.14433792 1.52549065
## r_species_id[ott207134,Intercept] -0.77765152 0.62230196
## r_species_id[ott215125,Intercept] 0.64861547 1.95361160
## r_species_id[ott216694,Intercept] -1.53393486 0.01503203
## r_species_id[ott223669,Intercept] 1.61543724 2.87338553
## r_species_id[ott225275,Intercept] 0.32892830 1.65094740
## r_species_id[ott237608,Intercept] -0.86114235 0.62152640
## r_species_id[ott246046,Intercept] -0.48902720 1.00147471
## r_species_id[ott247341,Intercept] 1.03744799 2.35173952
## r_species_id[ott256062,Intercept] -1.26405493 0.43963016
## r_species_id[ott256089,Intercept] -1.20592563 0.31209290
## r_species_id[ott256145,Intercept] -1.38196373 0.24313468
## r_species_id[ott263960,Intercept] 0.19082509 1.53724230
## r_species_id[ott263980,Intercept] -1.33040510 0.23947962
## r_species_id[ott263987,Intercept] -1.17190971 0.38548563
## r_species_id[ott263988,Intercept] -1.13163073 0.29278774
## r_species_id[ott265121,Intercept] -1.24320810 0.20253752
## r_species_id[ott266342,Intercept] -1.36630923 0.40968236
## r_species_id[ott269063,Intercept] -1.12946746 0.66666606
## r_species_id[ott275893,Intercept] -0.84805861 0.59875112
## r_species_id[ott275897,Intercept] -0.80122901 0.66617903
## r_species_id[ott2810724,Intercept] -0.96739798 0.54297606
## r_species_id[ott2819986,Intercept] -1.22185696 0.31079260
## r_species_id[ott2821097,Intercept] -1.02981540 0.40361865
## r_species_id[ott2844172,Intercept] 0.72688051 2.07436030
## r_species_id[ott2844962,Intercept] 0.89093112 2.24529388
## r_species_id[ott2849837,Intercept] 0.48287936 1.89595681
## r_species_id[ott2942244,Intercept] 0.68467695 2.03729779
## r_species_id[ott316441,Intercept] 1.00364126 2.26744021
## r_species_id[ott33153,Intercept] -1.13250537 0.41673174
## r_species_id[ott336388,Intercept] -1.23343695 0.25800990
## r_species_id[ott34559,Intercept] -1.17802615 0.32033645
## r_species_id[ott346740,Intercept] 0.16469813 1.49654199
## r_species_id[ott3583594,Intercept] 1.00505255 2.32889789
## r_species_id[ott3587677,Intercept] 1.38502618 2.70701487
## r_species_id[ott359012,Intercept] -1.28289076 0.51291238
## r_species_id[ott361837,Intercept] -1.37335468 0.24318532
## r_species_id[ott362913,Intercept] 0.48312544 1.83591674
## r_species_id[ott365439,Intercept] 0.40255151 1.79062209
## r_species_id[ott3663378,Intercept] 0.08187820 1.43049556
## r_species_id[ott3665433,Intercept] 0.43788683 1.80693197
## r_species_id[ott3684291,Intercept] -0.24216242 1.20120919
## r_species_id[ott3684365,Intercept] -0.36931176 1.16121537
## r_species_id[ott3684379,Intercept] -0.19918394 1.38405815
## r_species_id[ott3684389,Intercept] -0.33108425 1.25285075
## r_species_id[ott3684437,Intercept] -0.27578204 1.22958296
## r_species_id[ott381979,Intercept] -1.04545406 0.63561809
## r_species_id[ott381980,Intercept] -1.24004368 0.26168235
## r_species_id[ott381983,Intercept] -1.37145983 0.26691652
## r_species_id[ott395048,Intercept] 1.38844194 2.70310868
## r_species_id[ott3974169,Intercept] 0.51575995 1.82435805
## r_species_id[ott3995126,Intercept] 0.48354530 1.79740115
## r_species_id[ott4010019,Intercept] -0.60739751 0.75980559
## r_species_id[ott4010960,Intercept] -0.62949393 0.72808455
## r_species_id[ott4011155,Intercept] -0.45138199 0.94079565
## r_species_id[ott4013437,Intercept] -0.55061142 0.82151703
## r_species_id[ott4013674,Intercept] -0.72332831 0.63795576
## r_species_id[ott4013684,Intercept] -0.65592366 0.70379857
## r_species_id[ott422679,Intercept] -0.82582233 0.59014467
## r_species_id[ott431388,Intercept] 0.40038677 1.76297514
## r_species_id[ott446088,Intercept] 0.99754616 2.31467892
## r_species_id[ott4741377,Intercept] -0.58867246 0.79431324
## r_species_id[ott4742064,Intercept] -0.62935609 0.72588171
## r_species_id[ott481952,Intercept] 0.48447259 1.79544941
## r_species_id[ott48288,Intercept] 1.07024335 2.34161958
## r_species_id[ott485470,Intercept] -1.21578319 0.28764984
## r_species_id[ott485473,Intercept] -1.25488105 0.43221463
## r_species_id[ott485476,Intercept] -1.26229607 0.25309193
## r_species_id[ott485480,Intercept] -1.37399221 0.37689246
## r_species_id[ott485482,Intercept] -1.30469304 0.36155494
## r_species_id[ott486834,Intercept] -0.96202444 0.62999978
## r_species_id[ott490206,Intercept] 0.49892901 1.84477529
## r_species_id[ott492241,Intercept] 0.70364316 2.09119561
## r_species_id[ott497063,Intercept] -1.14451616 0.34525923
## r_species_id[ott4974308,Intercept] 0.56130781 1.89883687
## r_species_id[ott4978773,Intercept] 0.82478729 2.16574309
## r_species_id[ott4979583,Intercept] 0.74549191 2.11467926
## r_species_id[ott518643,Intercept] 0.53621089 1.90777856
## r_species_id[ott542509,Intercept] 1.42852281 2.69874858
## r_species_id[ott54768,Intercept] 0.08293999 1.47442248
## r_species_id[ott549846,Intercept] 0.40086766 1.74673573
## r_species_id[ott560703,Intercept] -0.82410859 0.76124890
## r_species_id[ott567703,Intercept] -0.63138404 0.73581209
## r_species_id[ott570365,Intercept] 0.03025473 1.45846591
## r_species_id[ott570656,Intercept] 0.24602508 1.60250875
## r_species_id[ott588761,Intercept] 0.41574981 1.77535901
## r_species_id[ott592355,Intercept] -0.23632126 1.14243191
## r_species_id[ott601255,Intercept] -1.59412895 -0.04043146
## r_species_id[ott602180,Intercept] -1.09704273 0.60984004
## r_species_id[ott60470,Intercept] -1.38834310 0.23207451
## r_species_id[ott60473,Intercept] -1.37317344 0.23147113
## r_species_id[ott60479,Intercept] -1.26760724 0.25514981
## r_species_id[ott633708,Intercept] -1.30118089 0.38900256
## r_species_id[ott633710,Intercept] -1.23677470 0.26369651
## r_species_id[ott633711,Intercept] -1.33297310 0.35071271
## r_species_id[ott633717,Intercept] -1.29904514 0.49538443
## r_species_id[ott633719,Intercept] -1.23374461 0.27522232
## r_species_id[ott643237,Intercept] 0.62644994 1.93118883
## r_species_id[ott645555,Intercept] -0.90802714 0.49156172
## r_species_id[ott649193,Intercept] -1.49149031 0.04658647
## r_species_id[ott675301,Intercept] 0.72806158 2.09556900
## r_species_id[ott724784,Intercept] -0.65147670 0.74264223
## r_species_id[ott72522,Intercept] -0.92380656 0.50722384
## r_species_id[ott727979,Intercept] -1.60748581 -0.03082879
## r_species_id[ott733462,Intercept] -0.41782073 0.98231458
## r_species_id[ott736728,Intercept] -0.84225179 0.71460551
## r_species_id[ott742128,Intercept] 0.21578808 1.54221763
## r_species_id[ott7489702,Intercept] 0.51028568 1.86821372
## r_species_id[ott7567530,Intercept] 0.03026486 1.44355950
## r_species_id[ott765113,Intercept] -0.76656775 0.61722355
## r_species_id[ott765280,Intercept] 0.14758048 1.49805535
## r_species_id[ott779028,Intercept] -0.74751544 0.63643802
## r_species_id[ott790395,Intercept] -1.13620773 0.29980708
## r_species_id[ott817791,Intercept] -1.02015806 0.44059896
## r_species_id[ott821356,Intercept] 0.29795671 1.62815496
## r_species_id[ott83430,Intercept] -1.07038325 0.36886410
## r_species_id[ott83432,Intercept] -1.06417982 0.37928588
## r_species_id[ott840001,Intercept] 0.59976537 1.90773366
## r_species_id[ott841027,Intercept] -1.37060482 0.42003493
## r_species_id[ott849781,Intercept] -1.29546499 0.28037609
## r_species_id[ott878345,Intercept] -1.17033057 0.42114902
## r_species_id[ott92556,Intercept] -0.85036469 0.56160472
## r_species_id[ott92561,Intercept] -0.92251290 0.48844157
## r_species_id[ott939432,Intercept] -1.12186313 0.33012440
## r_species_id[ott939454,Intercept] -1.12197393 0.33259937
## r_species_id[ott954042,Intercept] 0.37468730 1.67690522
## r_species_id[ott958293,Intercept] -1.29845250 0.33833056
## r_species_id[ott958304,Intercept] -1.36665888 0.31634816
## r_species_id[ott962359,Intercept] -1.08682718 0.55493372
## r_species_id[ott987480,Intercept] 0.79648474 2.17846157
## lp__ -598.80436333 -556.01021506plot(conditional_effects(phylo_fit_fission_cell_type, points = TRUE, ask = F)) 
hyp = hypothesis(phylo_fit_fission_cell_type, "FissionOrBuddingObserved_Genus_nominalno > FissionOrBuddingObserved_Genus_nominalyes") #test hypothesis that there is no difference based on coefficients
hyp## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (FissionOrBudding... > 0 -0.09 0.1 -0.25 0.08 0.23
## Post.Prob Star
## 1 0.19
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(hyp)
Does germline timing correlate with increased cell number?
#fit the model
phylofit_germline_cell_num<-
brm(data = df,
family=gaussian(), # family of model
formula = log(cell_number) ~ 0 + germline_timing_simple + (1|gr(species_id, cov = CovarMatrix)), #formula, the 0 means that there are estimates for both clonal and non-clonal, rather than relative to each other
data2 = list(CovarMatrix = CovarMatrix) ,
iter = 1000000, warmup = 100000, chains = 5,thin = 1000, cores = 5, #chain settings
prior = prior(normal(0, 10), "b"), #defining the priors- for things in 'class b' set this prior. (can also use get_priors() and set_priors() fucncitons)
file = 'fits/fit_phy_GermCellNum' )
plot(phylofit_germline_cell_num) #check that chains converged
pp_check(phylofit_germline_cell_num) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
plot(conditional_effects(phylofit_germline_cell_num), points = TRUE) 
summary(phylofit_germline_cell_num)## Warning: Parts of the model have not converged (some Rhats are > 1.05). Be
## careful when analysing the results! We recommend running more iterations and/or
## setting stronger priors.## Warning: There were 1219 divergent transitions after warmup.
## Increasing adapt_delta above 0.8 may help. See http://mc-stan.org/misc/
## warnings.html#divergent-transitions-after-warmup## Family: gaussian
## Links: mu = identity; sigma = identity
## Formula: log(cell_number) ~ 0 + germline_timing_simple + (1 | gr(species_id, cov = CovarMatrix))
## Data: df (Number of observations: 158)
## Samples: 5 chains, each with iter = 1e+06; warmup = 1e+05; thin = 1000;
## total post-warmup samples = 4500
##
## Group-Level Effects:
## ~species_id (Number of levels: 158)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 3.07 0.23 2.60 3.54 1.01 979 1539
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## germline_timing_simple 3.39 3.62 -3.72 10.58 1.05
## germline_timing_simpleadult 7.16 2.85 1.79 12.78 1.01
## germline_timing_simpleearly 8.03 3.54 0.75 14.68 1.07
## germline_timing_simpleno_germline 1.67 3.06 -4.26 7.68 1.01
## Bulk_ESS Tail_ESS
## germline_timing_simple 733 756
## germline_timing_simpleadult 454 1575
## germline_timing_simpleearly 908 1652
## germline_timing_simpleno_germline 516 604
##
## Family Specific Parameters:
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sigma 1.07 0.62 0.16 2.42 1.13 26 15
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).pairs(phylofit_germline_cell_num)
posterior_summary(phylofit_germline_cell_num, robust = T) #what are the medians for coefficients? if F, then returns means## Estimate Est.Error Q2.5
## b_germline_timing_simple 3.42609702 3.6456708 -3.7159027
## b_germline_timing_simpleadult 6.94899423 2.7580361 1.7860413
## b_germline_timing_simpleearly 7.92744534 3.4688554 0.7547506
## b_germline_timing_simpleno_germline 1.45206392 2.9379109 -4.2551620
## sd_species_id__Intercept 3.06991955 0.2213336 2.6024103
## sigma 0.99502613 0.6726738 0.1572766
## r_species_id[ott1002450,Intercept] 1.99248457 3.1427736 -4.6126736
## r_species_id[ott1017821,Intercept] -2.44198237 2.8902686 -8.7713890
## r_species_id[ott1052546,Intercept] -2.49751459 2.9512065 -8.6516385
## r_species_id[ott1059898,Intercept] 2.69648232 2.9565248 -3.5877554
## r_species_id[ott1059900,Intercept] 4.42144197 2.8602242 -1.7698357
## r_species_id[ott1061937,Intercept] 5.21777302 3.2056694 -1.2970632
## r_species_id[ott1069171,Intercept] 12.89776745 3.0907144 6.3493268
## r_species_id[ott1072227,Intercept] 1.36479575 3.1496192 -5.2944196
## r_species_id[ott108923,Intercept] 6.42116650 2.8962205 0.0585727
## r_species_id[ott1099013,Intercept] 16.71123899 2.9976380 10.3016515
## r_species_id[ott111442,Intercept] 4.14997692 2.9174191 -2.3898715
## r_species_id[ott112015,Intercept] 1.38380173 3.1223632 -5.3487446
## r_species_id[ott112016,Intercept] 1.32546634 3.1646132 -5.2844625
## r_species_id[ott112017,Intercept] 1.39743015 3.1020625 -5.2775496
## r_species_id[ott127047,Intercept] -1.32046531 2.9488586 -7.6543353
## r_species_id[ott150272,Intercept] 0.91356133 3.6896357 -6.1488996
## r_species_id[ott160850,Intercept] 4.28155395 3.0200759 -2.0567609
## r_species_id[ott165368,Intercept] 18.26608355 3.6564003 11.0940846
## r_species_id[ott167121,Intercept] 1.97279873 3.1423902 -4.5767761
## r_species_id[ott178177,Intercept] 11.49194801 2.9418538 5.3123222
## r_species_id[ott178412,Intercept] 21.65296687 3.6160306 14.5234964
## r_species_id[ott181933,Intercept] 20.88945002 3.0706221 14.4472348
## r_species_id[ott182906,Intercept] 17.85575975 3.7717719 10.2898994
## r_species_id[ott186999,Intercept] 15.48258608 3.0682561 8.9880251
## r_species_id[ott187583,Intercept] 0.48420714 3.7079441 -6.7719007
## r_species_id[ott199292,Intercept] 8.15618309 2.9052406 1.7394949
## r_species_id[ott207134,Intercept] 14.82046344 3.0890899 8.2342038
## r_species_id[ott215125,Intercept] 17.04780517 3.0296852 10.6943253
## r_species_id[ott216694,Intercept] 20.57633699 2.9991104 14.2350655
## r_species_id[ott223669,Intercept] 18.32127823 3.5780760 11.1501061
## r_species_id[ott225275,Intercept] 15.95346285 3.0387438 9.6030852
## r_species_id[ott237608,Intercept] 13.61451538 3.1159507 7.2030857
## r_species_id[ott246046,Intercept] 2.86706788 2.9690903 -3.3822520
## r_species_id[ott247341,Intercept] 23.13567656 3.7908746 15.5790049
## r_species_id[ott256062,Intercept] -1.85228487 3.0312856 -8.1407918
## r_species_id[ott256089,Intercept] -2.73974852 2.9268494 -9.0407435
## r_species_id[ott256145,Intercept] 1.94302256 3.1780957 -4.6403117
## r_species_id[ott263960,Intercept] 13.47673814 2.9594704 7.1377876
## r_species_id[ott263980,Intercept] 13.44234583 2.8967691 7.0527766
## r_species_id[ott263987,Intercept] 10.20848233 2.9407667 3.8640036
## r_species_id[ott263988,Intercept] 17.08183159 2.9831368 10.8030290
## r_species_id[ott265121,Intercept] 18.61695575 3.2678123 12.0950826
## r_species_id[ott266342,Intercept] -0.29619258 3.6732210 -7.3026446
## r_species_id[ott269063,Intercept] -3.56660684 2.9687525 -9.9243034
## r_species_id[ott275893,Intercept] 13.30450586 2.9866750 6.9807543
## r_species_id[ott275897,Intercept] 13.50833109 3.0039604 7.0727087
## r_species_id[ott2810724,Intercept] 2.76668807 3.0895863 -3.6323004
## r_species_id[ott2819986,Intercept] 16.96108671 3.1398783 10.4588710
## r_species_id[ott2821097,Intercept] 15.69010390 2.9406497 9.3759467
## r_species_id[ott2844172,Intercept] 1.50774439 2.8509191 -4.6514498
## r_species_id[ott2844962,Intercept] -0.57565840 3.7060614 -7.6647618
## r_species_id[ott2849837,Intercept] 3.46389802 3.6532630 -3.7215632
## r_species_id[ott2942244,Intercept] 3.90556403 3.5896535 -3.5817513
## r_species_id[ott316441,Intercept] 14.46891891 3.8060252 6.5829094
## r_species_id[ott33153,Intercept] 2.76654615 3.1500854 -3.8409630
## r_species_id[ott336388,Intercept] 20.19959878 2.9828704 13.8594616
## r_species_id[ott34559,Intercept] 15.93241800 3.0438044 9.6030731
## r_species_id[ott346740,Intercept] 17.62853833 3.0595821 11.1997227
## r_species_id[ott3583594,Intercept] 1.25776392 3.6156530 -5.7921409
## r_species_id[ott3587677,Intercept] -1.60144594 3.6725880 -8.7130917
## r_species_id[ott359012,Intercept] 1.15370950 3.1317373 -5.5065089
## r_species_id[ott361837,Intercept] 1.38683611 3.1577040 -5.1174253
## r_species_id[ott362913,Intercept] 3.64090147 2.9667473 -2.5892624
## r_species_id[ott365439,Intercept] 2.64362445 3.6546246 -4.4362197
## r_species_id[ott3663378,Intercept] 11.47421845 3.0462018 5.1049713
## r_species_id[ott3665433,Intercept] 5.81339435 2.9501901 -0.6665857
## r_species_id[ott3684291,Intercept] 3.68059006 3.6682166 -3.6402663
## r_species_id[ott3684365,Intercept] 3.68524142 3.8216736 -3.7348955
## r_species_id[ott3684379,Intercept] -0.26437453 3.7188512 -7.7094875
## r_species_id[ott3684389,Intercept] 1.87115411 3.7640400 -5.5216986
## r_species_id[ott3684437,Intercept] 0.10531841 3.7667654 -7.3671871
## r_species_id[ott381979,Intercept] 0.07545832 3.1714416 -6.4692737
## r_species_id[ott381980,Intercept] 3.15061568 3.0181040 -3.0659893
## r_species_id[ott381983,Intercept] 1.42510730 3.1601900 -5.2907148
## r_species_id[ott395048,Intercept] -0.91550393 3.6129023 -8.1141451
## r_species_id[ott3974169,Intercept] 16.42041740 2.9891857 10.1048277
## r_species_id[ott3995126,Intercept] 17.25348914 2.9784545 10.9391138
## r_species_id[ott4010019,Intercept] 16.89671441 3.0527541 10.5078719
## r_species_id[ott4010960,Intercept] 17.51016712 3.0617215 11.2412477
## r_species_id[ott4011155,Intercept] 9.62347559 2.9721812 3.3378038
## r_species_id[ott4013437,Intercept] 14.71955896 2.9335587 8.3782738
## r_species_id[ott4013674,Intercept] 18.70181814 3.2871587 12.1143216
## r_species_id[ott4013684,Intercept] 17.51054434 3.0075544 11.1886814
## r_species_id[ott422679,Intercept] -0.97423142 2.9103515 -7.2017202
## r_species_id[ott431388,Intercept] 6.97739851 2.8877422 0.6814831
## r_species_id[ott446088,Intercept] 1.32319190 3.5773095 -5.7996969
## r_species_id[ott4741377,Intercept] 13.99963649 2.8507502 7.6265131
## r_species_id[ott4742064,Intercept] 18.51964072 3.1167009 12.1215465
## r_species_id[ott481952,Intercept] 17.76931948 3.1216327 11.3128112
## r_species_id[ott48288,Intercept] 13.97031156 3.6629563 6.6845715
## r_species_id[ott485470,Intercept] 0.80390618 2.9314680 -5.4854365
## r_species_id[ott485473,Intercept] -1.94982316 2.9416871 -8.3141669
## r_species_id[ott485476,Intercept] 2.03448142 3.1948426 -4.5187330
## r_species_id[ott485480,Intercept] 2.85573507 2.8905770 -3.4734327
## r_species_id[ott485482,Intercept] -0.19741340 3.6021613 -7.2245065
## r_species_id[ott486834,Intercept] -0.48002539 2.9151835 -6.6600791
## r_species_id[ott490206,Intercept] 6.07762948 2.9605807 -0.2296249
## r_species_id[ott492241,Intercept] 0.41730912 3.5978065 -6.8361696
## r_species_id[ott497063,Intercept] 15.25999836 2.9443174 8.8406783
## r_species_id[ott4974308,Intercept] 3.53660223 3.7307520 -3.4657145
## r_species_id[ott4978773,Intercept] 0.37881144 3.5804178 -6.9353975
## r_species_id[ott4979583,Intercept] 1.00861474 3.6287590 -6.2045681
## r_species_id[ott518643,Intercept] 2.75854245 3.6555856 -4.4680771
## r_species_id[ott542509,Intercept] 18.18213933 3.6517812 11.1272922
## r_species_id[ott54768,Intercept] 8.18420835 2.8923099 1.9507345
## r_species_id[ott549846,Intercept] 4.74083427 3.6619935 -2.4375890
## r_species_id[ott560703,Intercept] -1.63007544 2.9447662 -7.8502168
## r_species_id[ott567703,Intercept] 16.55500608 2.9938550 10.2395458
## r_species_id[ott570365,Intercept] -0.59361246 2.9089921 -6.8047860
## r_species_id[ott570656,Intercept] 10.74616928 2.8642878 4.3919411
## r_species_id[ott588761,Intercept] 2.33120224 2.8842811 -3.8938261
## r_species_id[ott592355,Intercept] 13.40483816 3.0234342 6.9420724
## r_species_id[ott601255,Intercept] 21.69473354 3.0268391 15.5007789
## r_species_id[ott602180,Intercept] 2.93457208 3.1365028 -3.5999943
## r_species_id[ott60470,Intercept] 1.39618962 3.1203269 -5.2535733
## r_species_id[ott60471,Intercept] 0.16468220 3.1121325 -6.5743876
## r_species_id[ott60473,Intercept] 1.35678423 3.0928190 -5.2429519
## r_species_id[ott60477,Intercept] 1.97370612 3.1279173 -4.5848178
## r_species_id[ott60479,Intercept] 1.96210543 3.1961951 -4.6032556
## r_species_id[ott633708,Intercept] 1.27321610 2.9592504 -5.0549390
## r_species_id[ott633710,Intercept] 3.10589836 3.0613512 -3.2264370
## r_species_id[ott633711,Intercept] 2.19313503 3.0130682 -4.1751793
## r_species_id[ott633717,Intercept] -1.75346194 2.9318813 -7.8962363
## r_species_id[ott633719,Intercept] 2.51462638 2.9838484 -3.4990162
## r_species_id[ott643237,Intercept] 16.96590634 2.9280293 10.6062376
## r_species_id[ott645555,Intercept] 18.04690989 3.1404421 11.5311445
## r_species_id[ott649193,Intercept] 17.15458996 2.9779022 10.9212025
## r_species_id[ott675301,Intercept] 4.57942842 3.7989146 -2.9202518
## r_species_id[ott724784,Intercept] 9.19116642 2.9593729 2.7569315
## r_species_id[ott72522,Intercept] 20.04617326 3.1756108 13.6771267
## r_species_id[ott727979,Intercept] 20.20716137 2.9584707 14.0357246
## r_species_id[ott733462,Intercept] 15.32888217 2.9895088 8.9487199
## r_species_id[ott736728,Intercept] 6.04643791 2.8926198 -0.2394106
## r_species_id[ott742128,Intercept] 2.37489489 2.9415381 -3.8099022
## r_species_id[ott7489702,Intercept] 3.31325834 3.6790343 -3.9640158
## r_species_id[ott7567530,Intercept] 4.33282817 2.9681354 -1.9628493
## r_species_id[ott765113,Intercept] -0.02461901 2.8583603 -6.4118349
## r_species_id[ott765280,Intercept] 12.41985895 2.8923847 6.1641610
## r_species_id[ott779028,Intercept] 14.97761511 2.9209996 8.7158426
## r_species_id[ott790395,Intercept] 15.89123643 2.9848584 9.6201912
## r_species_id[ott817791,Intercept] 14.63765678 2.9837927 8.4801986
## r_species_id[ott821356,Intercept] 13.71334146 2.9260621 7.5753622
## r_species_id[ott83430,Intercept] 11.93109994 2.8644073 5.5938699
## r_species_id[ott83432,Intercept] 4.08223448 3.1574516 -2.1933020
## r_species_id[ott840001,Intercept] 16.97426602 2.9690336 10.5690754
## r_species_id[ott841027,Intercept] -0.21681980 3.6640707 -7.3510218
## r_species_id[ott849781,Intercept] 14.31689554 3.0814300 7.8551707
## r_species_id[ott878345,Intercept] 6.47210781 2.9256936 0.1275615
## r_species_id[ott92556,Intercept] 11.79522305 2.9340454 5.4919601
## r_species_id[ott92561,Intercept] 14.53863187 3.0413480 8.1480597
## r_species_id[ott939432,Intercept] 4.43490975 2.9992834 -1.6122673
## r_species_id[ott939454,Intercept] 4.42285415 2.9029234 -1.8886801
## r_species_id[ott954042,Intercept] 15.17407791 3.8380761 7.4936067
## r_species_id[ott958293,Intercept] 1.92558216 3.1350384 -4.6770866
## r_species_id[ott958304,Intercept] 1.33031325 3.1731860 -5.2235961
## r_species_id[ott962359,Intercept] 1.34967534 2.9298475 -5.0495190
## r_species_id[ott987480,Intercept] 4.09015188 3.7093487 -3.3000607
## r_species_id[ott989764,Intercept] 6.74657781 2.9847310 0.3861690
## lp__ -464.69202078 107.0026800 -603.9722980
## Q97.5
## b_germline_timing_simple 10.584152
## b_germline_timing_simpleadult 12.777312
## b_germline_timing_simpleearly 14.678657
## b_germline_timing_simpleno_germline 7.678403
## sd_species_id__Intercept 3.540341
## sigma 2.421592
## r_species_id[ott1002450,Intercept] 8.080142
## r_species_id[ott1017821,Intercept] 3.096677
## r_species_id[ott1052546,Intercept] 3.092212
## r_species_id[ott1059898,Intercept] 8.360771
## r_species_id[ott1059900,Intercept] 10.018602
## r_species_id[ott1061937,Intercept] 11.290418
## r_species_id[ott1069171,Intercept] 18.360529
## r_species_id[ott1072227,Intercept] 7.485817
## r_species_id[ott108923,Intercept] 12.115871
## r_species_id[ott1099013,Intercept] 22.360271
## r_species_id[ott111442,Intercept] 9.765671
## r_species_id[ott112015,Intercept] 7.414733
## r_species_id[ott112016,Intercept] 7.569822
## r_species_id[ott112017,Intercept] 7.568846
## r_species_id[ott127047,Intercept] 4.486614
## r_species_id[ott150272,Intercept] 8.339389
## r_species_id[ott160850,Intercept] 10.036152
## r_species_id[ott165368,Intercept] 25.491013
## r_species_id[ott167121,Intercept] 8.106051
## r_species_id[ott178177,Intercept] 16.824146
## r_species_id[ott178412,Intercept] 27.739002
## r_species_id[ott181933,Intercept] 26.518745
## r_species_id[ott182906,Intercept] 25.421414
## r_species_id[ott186999,Intercept] 20.964725
## r_species_id[ott187583,Intercept] 7.796460
## r_species_id[ott199292,Intercept] 13.881953
## r_species_id[ott207134,Intercept] 20.395623
## r_species_id[ott215125,Intercept] 22.608542
## r_species_id[ott216694,Intercept] 26.236077
## r_species_id[ott223669,Intercept] 25.674663
## r_species_id[ott225275,Intercept] 21.583097
## r_species_id[ott237608,Intercept] 19.229417
## r_species_id[ott246046,Intercept] 8.558250
## r_species_id[ott247341,Intercept] 30.538422
## r_species_id[ott256062,Intercept] 3.632911
## r_species_id[ott256089,Intercept] 2.963400
## r_species_id[ott256145,Intercept] 8.124576
## r_species_id[ott263960,Intercept] 18.978043
## r_species_id[ott263980,Intercept] 19.045404
## r_species_id[ott263987,Intercept] 15.909342
## r_species_id[ott263988,Intercept] 22.690679
## r_species_id[ott265121,Intercept] 24.221400
## r_species_id[ott266342,Intercept] 7.269712
## r_species_id[ott269063,Intercept] 2.086037
## r_species_id[ott275893,Intercept] 18.882209
## r_species_id[ott275897,Intercept] 19.213665
## r_species_id[ott2810724,Intercept] 9.034601
## r_species_id[ott2819986,Intercept] 22.726545
## r_species_id[ott2821097,Intercept] 21.355180
## r_species_id[ott2844172,Intercept] 7.200757
## r_species_id[ott2844962,Intercept] 6.831467
## r_species_id[ott2849837,Intercept] 10.873544
## r_species_id[ott2942244,Intercept] 11.086764
## r_species_id[ott316441,Intercept] 21.974262
## r_species_id[ott33153,Intercept] 8.871727
## r_species_id[ott336388,Intercept] 25.708741
## r_species_id[ott34559,Intercept] 21.650336
## r_species_id[ott346740,Intercept] 23.188794
## r_species_id[ott3583594,Intercept] 8.921274
## r_species_id[ott3587677,Intercept] 5.917915
## r_species_id[ott359012,Intercept] 7.242999
## r_species_id[ott361837,Intercept] 7.531543
## r_species_id[ott362913,Intercept] 9.283738
## r_species_id[ott365439,Intercept] 9.964813
## r_species_id[ott3663378,Intercept] 17.027642
## r_species_id[ott3665433,Intercept] 11.271034
## r_species_id[ott3684291,Intercept] 10.977956
## r_species_id[ott3684365,Intercept] 11.288833
## r_species_id[ott3684379,Intercept] 7.490038
## r_species_id[ott3684389,Intercept] 9.599077
## r_species_id[ott3684437,Intercept] 7.544370
## r_species_id[ott381979,Intercept] 6.104060
## r_species_id[ott381980,Intercept] 8.687702
## r_species_id[ott381983,Intercept] 7.421631
## r_species_id[ott395048,Intercept] 6.611994
## r_species_id[ott3974169,Intercept] 22.042064
## r_species_id[ott3995126,Intercept] 22.965367
## r_species_id[ott4010019,Intercept] 22.548945
## r_species_id[ott4010960,Intercept] 23.248067
## r_species_id[ott4011155,Intercept] 15.354980
## r_species_id[ott4013437,Intercept] 20.304470
## r_species_id[ott4013674,Intercept] 24.359384
## r_species_id[ott4013684,Intercept] 23.145865
## r_species_id[ott422679,Intercept] 4.738644
## r_species_id[ott431388,Intercept] 12.553543
## r_species_id[ott446088,Intercept] 8.858681
## r_species_id[ott4741377,Intercept] 19.513781
## r_species_id[ott4742064,Intercept] 24.255354
## r_species_id[ott481952,Intercept] 23.459936
## r_species_id[ott48288,Intercept] 21.313354
## r_species_id[ott485470,Intercept] 6.318466
## r_species_id[ott485473,Intercept] 3.663854
## r_species_id[ott485476,Intercept] 8.266321
## r_species_id[ott485480,Intercept] 8.371440
## r_species_id[ott485482,Intercept] 7.142573
## r_species_id[ott486834,Intercept] 5.051734
## r_species_id[ott490206,Intercept] 11.590345
## r_species_id[ott492241,Intercept] 7.724865
## r_species_id[ott497063,Intercept] 20.789252
## r_species_id[ott4974308,Intercept] 11.307968
## r_species_id[ott4978773,Intercept] 7.895545
## r_species_id[ott4979583,Intercept] 8.408380
## r_species_id[ott518643,Intercept] 10.140600
## r_species_id[ott542509,Intercept] 25.670022
## r_species_id[ott54768,Intercept] 13.626398
## r_species_id[ott549846,Intercept] 12.427059
## r_species_id[ott560703,Intercept] 4.096789
## r_species_id[ott567703,Intercept] 22.038235
## r_species_id[ott570365,Intercept] 5.349493
## r_species_id[ott570656,Intercept] 16.363404
## r_species_id[ott588761,Intercept] 8.044289
## r_species_id[ott592355,Intercept] 18.994390
## r_species_id[ott601255,Intercept] 27.258008
## r_species_id[ott602180,Intercept] 9.010865
## r_species_id[ott60470,Intercept] 7.548191
## r_species_id[ott60471,Intercept] 6.270965
## r_species_id[ott60473,Intercept] 7.631676
## r_species_id[ott60477,Intercept] 8.200983
## r_species_id[ott60479,Intercept] 8.055455
## r_species_id[ott633708,Intercept] 7.047696
## r_species_id[ott633710,Intercept] 8.671270
## r_species_id[ott633711,Intercept] 7.744483
## r_species_id[ott633717,Intercept] 3.987609
## r_species_id[ott633719,Intercept] 8.202503
## r_species_id[ott643237,Intercept] 22.423775
## r_species_id[ott645555,Intercept] 23.765726
## r_species_id[ott649193,Intercept] 22.966079
## r_species_id[ott675301,Intercept] 11.922598
## r_species_id[ott724784,Intercept] 15.069359
## r_species_id[ott72522,Intercept] 25.684691
## r_species_id[ott727979,Intercept] 25.941179
## r_species_id[ott733462,Intercept] 21.054448
## r_species_id[ott736728,Intercept] 11.946389
## r_species_id[ott742128,Intercept] 7.965908
## r_species_id[ott7489702,Intercept] 10.748996
## r_species_id[ott7567530,Intercept] 10.175107
## r_species_id[ott765113,Intercept] 5.536461
## r_species_id[ott765280,Intercept] 18.051418
## r_species_id[ott779028,Intercept] 20.673285
## r_species_id[ott790395,Intercept] 21.519641
## r_species_id[ott817791,Intercept] 20.208496
## r_species_id[ott821356,Intercept] 19.353658
## r_species_id[ott83430,Intercept] 17.731100
## r_species_id[ott83432,Intercept] 11.018759
## r_species_id[ott840001,Intercept] 22.610799
## r_species_id[ott841027,Intercept] 7.302418
## r_species_id[ott849781,Intercept] 19.963530
## r_species_id[ott878345,Intercept] 12.482267
## r_species_id[ott92556,Intercept] 17.358841
## r_species_id[ott92561,Intercept] 20.181519
## r_species_id[ott939432,Intercept] 10.125099
## r_species_id[ott939454,Intercept] 9.987044
## r_species_id[ott954042,Intercept] 22.653094
## r_species_id[ott958293,Intercept] 7.839058
## r_species_id[ott958304,Intercept] 7.348908
## r_species_id[ott962359,Intercept] 7.056204
## r_species_id[ott987480,Intercept] 11.605380
## r_species_id[ott989764,Intercept] 12.362309
## lp__ -185.063333hyp = hypothesis(phylofit_germline_cell_num, c("germline_timing_simpleearly = germline_timing_simple", "germline_timing_simpleearly = germline_timing_simpleadult", "germline_timing_simpleearly = germline_timing_simpleno_germline")) #test hypothesis that there is no difference based on coefficients
hyp## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (germline_timing_... = 0 4.64 2.86 -1.12 10.49 NA
## 2 (germline_timing_... = 0 0.87 2.39 -4.02 5.51 NA
## 3 (germline_timing_... = 0 6.36 2.71 0.74 11.56 NA
## Post.Prob Star
## 1 NA
## 2 NA
## 3 NA *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(hyp)
Does germline timing correlate with increased cell types (per cell)?
fit_type_phy<-
brm(data = df,
family=poisson(),
formula = cell_types ~ 0 + germline_timing_simple + scale(log(cell_number)) + (1|gr(species_id, cov = CovarMatrix)),
iter = 1000000, warmup = 100000, chains = 5, thin = 1000, cores = 5,
prior = prior(normal(0, 10), "b"), file = 'fits/fit_phy_GermCellType', # same simple prior
data2 = list(CovarMatrix = CovarMatrix))
plot(fit_type_phy) #check that chains converged
pp_check(fit_type_phy) #check the predictions## Using 10 posterior samples for ppc type 'dens_overlay' by default.
summary(fit_type_phy) #summary of model ## Family: poisson
## Links: mu = log
## Formula: cell_types ~ 0 + germline_timing_simple + scale(log(cell_number)) + (1 | gr(species_id, cov = CovarMatrix))
## Data: df (Number of observations: 157)
## Samples: 5 chains, each with iter = 1e+06; warmup = 1e+05; thin = 1000;
## total post-warmup samples = 4500
##
## Group-Level Effects:
## ~species_id (Number of levels: 157)
## Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
## sd(Intercept) 0.19 0.02 0.15 0.24 1.00 4597 4296
##
## Population-Level Effects:
## Estimate Est.Error l-95% CI u-95% CI Rhat
## germline_timing_simple 1.60 0.35 0.89 2.26 1.00
## germline_timing_simpleadult 1.68 0.25 1.17 2.17 1.00
## germline_timing_simpleearly 2.22 0.32 1.58 2.82 1.00
## germline_timing_simpleno_germline 1.17 0.36 0.45 1.86 1.00
## scalelogcell_number 0.61 0.06 0.49 0.73 1.00
## Bulk_ESS Tail_ESS
## germline_timing_simple 4328 4042
## germline_timing_simpleadult 4395 4376
## germline_timing_simpleearly 4580 4207
## germline_timing_simpleno_germline 4673 4361
## scalelogcell_number 4593 4486
##
## Samples were drawn using sampling(NUTS). For each parameter, Bulk_ESS
## and Tail_ESS are effective sample size measures, and Rhat is the potential
## scale reduction factor on split chains (at convergence, Rhat = 1).posterior_summary(fit_type_phy, robust = T)## Estimate Est.Error Q2.5
## b_germline_timing_simple 1.613862e+00 0.34229785 8.900840e-01
## b_germline_timing_simpleadult 1.683416e+00 0.24941465 1.174107e+00
## b_germline_timing_simpleearly 2.212725e+00 0.31195555 1.583920e+00
## b_germline_timing_simpleno_germline 1.175380e+00 0.35251608 4.527177e-01
## b_scalelogcell_number 6.101713e-01 0.06365627 4.865558e-01
## sd_species_id__Intercept 1.895796e-01 0.02117920 1.512694e-01
## r_species_id[ott1017821,Intercept] -1.320057e-01 0.37621521 -8.752720e-01
## r_species_id[ott1052546,Intercept] -1.327601e-01 0.38675465 -8.700756e-01
## r_species_id[ott1059898,Intercept] 6.956797e-01 0.33521919 2.696798e-02
## r_species_id[ott1059900,Intercept] 7.127724e-01 0.32469653 6.029043e-02
## r_species_id[ott1061937,Intercept] -5.790327e-01 0.41726509 -1.415903e+00
## r_species_id[ott1069171,Intercept] 6.282759e-01 0.31541029 4.312902e-02
## r_species_id[ott1072227,Intercept] -3.835338e-01 0.40294286 -1.202793e+00
## r_species_id[ott108923,Intercept] 7.422910e-01 0.31742357 1.219379e-01
## r_species_id[ott1099013,Intercept] -4.105269e-01 0.32951555 -1.050182e+00
## r_species_id[ott111442,Intercept] 9.204121e-01 0.33209181 2.836621e-01
## r_species_id[ott112015,Intercept] -3.784360e-01 0.40328873 -1.209839e+00
## r_species_id[ott112016,Intercept] -3.755042e-01 0.40809570 -1.179680e+00
## r_species_id[ott112017,Intercept] -3.859473e-01 0.40661784 -1.183408e+00
## r_species_id[ott127047,Intercept] 6.900156e-01 0.33181285 2.906827e-02
## r_species_id[ott150272,Intercept] 1.000047e+00 0.35452628 3.291106e-01
## r_species_id[ott160850,Intercept] -2.773533e-01 0.37531692 -1.003980e+00
## r_species_id[ott165368,Intercept] 1.621118e+00 0.33568207 9.709559e-01
## r_species_id[ott167121,Intercept] -3.755459e-01 0.36739297 -1.118421e+00
## r_species_id[ott178177,Intercept] 1.721172e-01 0.33809655 -5.016634e-01
## r_species_id[ott178412,Intercept] 4.955721e-01 0.30554917 -1.132571e-01
## r_species_id[ott181933,Intercept] -3.338145e-01 0.34459883 -1.019382e+00
## r_species_id[ott182906,Intercept] 1.103289e+00 0.33851275 4.433805e-01
## r_species_id[ott186999,Intercept] 4.264693e-01 0.31764556 -2.236822e-01
## r_species_id[ott187583,Intercept] -8.278403e-01 0.39791756 -1.621770e+00
## r_species_id[ott199292,Intercept] 8.110085e-01 0.32797561 1.745638e-01
## r_species_id[ott207134,Intercept] -8.029681e-02 0.31328730 -6.952835e-01
## r_species_id[ott215125,Intercept] 1.319569e+00 0.29764254 7.397137e-01
## r_species_id[ott216694,Intercept] -7.410767e-01 0.35552224 -1.448605e+00
## r_species_id[ott223669,Intercept] 1.660082e+00 0.33954264 9.850681e-01
## r_species_id[ott225275,Intercept] 9.320068e-01 0.30422771 3.422907e-01
## r_species_id[ott237608,Intercept] -1.431173e-01 0.35030221 -8.315568e-01
## r_species_id[ott246046,Intercept] 2.789052e-01 0.33135693 -3.688767e-01
## r_species_id[ott247341,Intercept] 1.149895e+00 0.35331209 4.614924e-01
## r_species_id[ott256062,Intercept] -5.335395e-01 0.40136615 -1.309569e+00
## r_species_id[ott256089,Intercept] -4.657957e-01 0.35547547 -1.180120e+00
## r_species_id[ott256145,Intercept] -3.812313e-01 0.41271320 -1.168378e+00
## r_species_id[ott263960,Intercept] 8.690192e-01 0.29711291 2.839766e-01
## r_species_id[ott263980,Intercept] -5.503092e-01 0.36352350 -1.275152e+00
## r_species_id[ott263987,Intercept] -4.317320e-01 0.36556780 -1.152812e+00
## r_species_id[ott263988,Intercept] -4.413921e-01 0.33313591 -1.079220e+00
## r_species_id[ott265121,Intercept] -4.803899e-01 0.33271585 -1.152188e+00
## r_species_id[ott266342,Intercept] -8.651876e-01 0.41760990 -1.682602e+00
## r_species_id[ott269063,Intercept] -1.795505e-01 0.41737849 -1.028845e+00
## r_species_id[ott275893,Intercept] -1.398137e-01 0.34028910 -8.046466e-01
## r_species_id[ott275897,Intercept] -1.219377e-01 0.33452841 -7.651139e-01
## r_species_id[ott2810724,Intercept] -1.861746e-01 0.36157209 -8.784147e-01
## r_species_id[ott2819986,Intercept] -4.886254e-01 0.34975464 -1.190270e+00
## r_species_id[ott2821097,Intercept] -3.649568e-01 0.32391265 -1.008585e+00
## r_species_id[ott2844172,Intercept] 1.217628e+00 0.32187949 5.919885e-01
## r_species_id[ott2844962,Intercept] 9.763933e-01 0.34538065 2.695796e-01
## r_species_id[ott2849837,Intercept] 8.672245e-01 0.36532180 1.847016e-01
## r_species_id[ott2942244,Intercept] 8.027351e-01 0.34314852 9.865480e-02
## r_species_id[ott316441,Intercept] 1.018003e+00 0.34516340 3.460523e-01
## r_species_id[ott33153,Intercept] -2.441729e-01 0.37159114 -9.815870e-01
## r_species_id[ott336388,Intercept] -5.035005e-01 0.35713778 -1.200381e+00
## r_species_id[ott34559,Intercept] -4.569033e-01 0.34296789 -1.127589e+00
## r_species_id[ott346740,Intercept] 8.581557e-01 0.30518910 2.694688e-01
## r_species_id[ott3583594,Intercept] 1.055770e+00 0.35978976 3.491975e-01
## r_species_id[ott3587677,Intercept] 1.360876e+00 0.35913420 6.611012e-01
## r_species_id[ott359012,Intercept] -3.615247e-01 0.43186647 -1.237853e+00
## r_species_id[ott361837,Intercept] -3.788062e-01 0.40723288 -1.186241e+00
## r_species_id[ott362913,Intercept] 1.002142e+00 0.31929447 3.802148e-01
## r_species_id[ott365439,Intercept] 4.892054e-01 0.37033641 -2.386455e-01
## r_species_id[ott3663378,Intercept] 6.216634e-01 0.31762264 -6.556566e-03
## r_species_id[ott3665433,Intercept] 9.649679e-01 0.32205313 3.367407e-01
## r_species_id[ott3684291,Intercept] 4.352979e-01 0.35561451 -2.601264e-01
## r_species_id[ott3684365,Intercept] 3.614566e-01 0.38175033 -4.359073e-01
## r_species_id[ott3684379,Intercept] 5.190988e-01 0.39660627 -2.902331e-01
## r_species_id[ott3684389,Intercept] 3.998355e-01 0.41107950 -4.162766e-01
## r_species_id[ott3684437,Intercept] 4.208434e-01 0.37629767 -2.983627e-01
## r_species_id[ott381979,Intercept] -1.154885e-01 0.38870627 -8.899393e-01
## r_species_id[ott381980,Intercept] -4.979959e-01 0.34588837 -1.198349e+00
## r_species_id[ott381983,Intercept] -3.755452e-01 0.39921426 -1.200578e+00
## r_species_id[ott395048,Intercept] 1.361184e+00 0.36434368 6.727905e-01
## r_species_id[ott3974169,Intercept] 1.199039e+00 0.29873822 6.109624e-01
## r_species_id[ott3995126,Intercept] 1.166316e+00 0.30252630 5.934373e-01
## r_species_id[ott4010019,Intercept] 6.262726e-02 0.30879574 -5.419050e-01
## r_species_id[ott4010960,Intercept] 7.085555e-02 0.31338406 -5.468632e-01
## r_species_id[ott4011155,Intercept] 2.251828e-01 0.32349423 -3.869468e-01
## r_species_id[ott4013437,Intercept] 1.374208e-01 0.30652330 -4.715778e-01
## r_species_id[ott4013674,Intercept] -6.224201e-03 0.30889343 -6.187875e-01
## r_species_id[ott4013684,Intercept] 4.780737e-02 0.30436749 -5.549839e-01
## r_species_id[ott422679,Intercept] -1.152730e-01 0.32592483 -7.726485e-01
## r_species_id[ott431388,Intercept] 1.049885e+00 0.30867806 4.612936e-01
## r_species_id[ott446088,Intercept] 1.043963e+00 0.36168583 3.396957e-01
## r_species_id[ott4741377,Intercept] 1.052793e-01 0.30892501 -5.146019e-01
## r_species_id[ott4742064,Intercept] 6.431004e-02 0.30412416 -5.348935e-01
## r_species_id[ott481952,Intercept] 1.146571e+00 0.29415384 5.721267e-01
## r_species_id[ott48288,Intercept] 1.107873e+00 0.32968562 4.309378e-01
## r_species_id[ott485470,Intercept] -4.882041e-01 0.34356081 -1.189752e+00
## r_species_id[ott485473,Intercept] -5.165923e-01 0.38599016 -1.305146e+00
## r_species_id[ott485476,Intercept] -3.763729e-01 0.36626065 -1.098182e+00
## r_species_id[ott485480,Intercept] -6.107805e-01 0.40102465 -1.445477e+00
## r_species_id[ott485482,Intercept] -7.653628e-01 0.38189203 -1.517469e+00
## r_species_id[ott486834,Intercept] -1.921825e-01 0.36292181 -9.047032e-01
## r_species_id[ott490206,Intercept] 9.988855e-01 0.31539995 3.760750e-01
## r_species_id[ott492241,Intercept] 8.215615e-01 0.35338947 1.085340e-01
## r_species_id[ott497063,Intercept] -4.437932e-01 0.33515440 -1.132447e+00
## r_species_id[ott4974308,Intercept] 6.617621e-01 0.36636565 -5.309627e-02
## r_species_id[ott4978773,Intercept] 8.676252e-01 0.35530820 1.651940e-01
## r_species_id[ott4979583,Intercept] 8.036261e-01 0.35878839 9.233255e-02
## r_species_id[ott518643,Intercept] 6.421357e-01 0.36493056 -6.747939e-02
## r_species_id[ott542509,Intercept] 1.479780e+00 0.34603074 8.173515e-01
## r_species_id[ott54768,Intercept] 7.593986e-01 0.31807427 1.402636e-01
## r_species_id[ott549846,Intercept] 5.178710e-01 0.36090529 -1.833122e-01
## r_species_id[ott560703,Intercept] -7.080214e-02 0.36688528 -7.996504e-01
## r_species_id[ott567703,Intercept] 7.581902e-02 0.31222969 -5.298788e-01
## r_species_id[ott570365,Intercept] 6.752173e-01 0.32013065 3.572399e-03
## r_species_id[ott570656,Intercept] 8.938374e-01 0.32398013 2.689032e-01
## r_species_id[ott588761,Intercept] 9.807428e-01 0.30945151 3.719624e-01
## r_species_id[ott592355,Intercept] 4.764503e-01 0.32153858 -1.374226e-01
## r_species_id[ott601255,Intercept] -7.612516e-01 0.35601852 -1.497443e+00
## r_species_id[ott602180,Intercept] -1.301254e-01 0.41326496 -9.575781e-01
## r_species_id[ott60470,Intercept] -3.855166e-01 0.40897077 -1.175136e+00
## r_species_id[ott60471,Intercept] -1.072685e-01 0.39265371 -9.074495e-01
## r_species_id[ott60473,Intercept] -3.838808e-01 0.40881099 -1.182631e+00
## r_species_id[ott60477,Intercept] -3.821318e-01 0.37251048 -1.101541e+00
## r_species_id[ott60479,Intercept] -3.721285e-01 0.37392424 -1.096923e+00
## r_species_id[ott633708,Intercept] -4.456668e-01 0.38843554 -1.238417e+00
## r_species_id[ott633710,Intercept] -5.010891e-01 0.34752457 -1.186866e+00
## r_species_id[ott633711,Intercept] -6.241796e-01 0.37813917 -1.384452e+00
## r_species_id[ott633717,Intercept] -4.001267e-01 0.41377820 -1.252042e+00
## r_species_id[ott633719,Intercept] -4.924346e-01 0.35136948 -1.185172e+00
## r_species_id[ott643237,Intercept] 1.232986e+00 0.30447116 6.363500e-01
## r_species_id[ott645555,Intercept] -2.509733e-01 0.32217828 -8.627770e-01
## r_species_id[ott649193,Intercept] -6.856349e-01 0.35606896 -1.392336e+00
## r_species_id[ott675301,Intercept] 1.249319e+00 0.36250297 5.436957e-01
## r_species_id[ott724784,Intercept] 4.950574e-02 0.31419472 -5.572837e-01
## r_species_id[ott72522,Intercept] -2.647577e-01 0.33374875 -9.039555e-01
## r_species_id[ott727979,Intercept] -7.710108e-01 0.37889118 -1.508077e+00
## r_species_id[ott733462,Intercept] 3.201453e-01 0.31965036 -3.379665e-01
## r_species_id[ott736728,Intercept] -1.176102e-01 0.34785485 -8.385756e-01
## r_species_id[ott742128,Intercept] 7.857412e-01 0.30946975 1.939340e-01
## r_species_id[ott7489702,Intercept] 5.737862e-01 0.35405220 -1.476073e-01
## r_species_id[ott7567530,Intercept] 7.010155e-01 0.32621781 4.275577e-02
## r_species_id[ott765113,Intercept] -8.737064e-02 0.31537223 -7.281576e-01
## r_species_id[ott765280,Intercept] 8.118104e-01 0.31400451 2.441184e-01
## r_species_id[ott779028,Intercept] -2.589175e-02 0.30707605 -6.320431e-01
## r_species_id[ott790395,Intercept] -4.518194e-01 0.33493644 -1.093026e+00
## r_species_id[ott817791,Intercept] -3.347904e-01 0.34170094 -9.949187e-01
## r_species_id[ott821356,Intercept] 9.635533e-01 0.30503384 3.720782e-01
## r_species_id[ott83430,Intercept] -3.983458e-01 0.32609437 -1.025249e+00
## r_species_id[ott83432,Intercept] -3.816801e-01 0.33931622 -1.022614e+00
## r_species_id[ott840001,Intercept] 1.263230e+00 0.30727429 6.878205e-01
## r_species_id[ott841027,Intercept] -8.636857e-01 0.40687239 -1.687187e+00
## r_species_id[ott849781,Intercept] -5.352084e-01 0.35590229 -1.269349e+00
## r_species_id[ott878345,Intercept] -4.286977e-01 0.35524953 -1.157656e+00
## r_species_id[ott92556,Intercept] -1.583363e-01 0.32098298 -8.005879e-01
## r_species_id[ott92561,Intercept] -2.352381e-01 0.32852658 -8.862393e-01
## r_species_id[ott939432,Intercept] -3.824572e-01 0.34521905 -1.062975e+00
## r_species_id[ott939454,Intercept] -3.718348e-01 0.33974750 -1.080271e+00
## r_species_id[ott954042,Intercept] 4.585395e-01 0.34190517 -2.342827e-01
## r_species_id[ott958293,Intercept] -4.720568e-01 0.37227548 -1.202426e+00
## r_species_id[ott958304,Intercept] -3.821677e-01 0.40086793 -1.182091e+00
## r_species_id[ott962359,Intercept] -2.769978e-01 0.37294406 -1.051810e+00
## r_species_id[ott987480,Intercept] 1.302857e+00 0.36066175 6.190324e-01
## r_species_id[ott989764,Intercept] 3.422009e-01 0.29939534 -2.403670e-01
## lp__ -5.919607e+02 10.93706128 -6.146191e+02
## Q97.5
## b_germline_timing_simple 2.260670e+00
## b_germline_timing_simpleadult 2.167713e+00
## b_germline_timing_simpleearly 2.819800e+00
## b_germline_timing_simpleno_germline 1.862193e+00
## b_scalelogcell_number 7.325088e-01
## sd_species_id__Intercept 2.354486e-01
## r_species_id[ott1017821,Intercept] 6.066463e-01
## r_species_id[ott1052546,Intercept] 5.961988e-01
## r_species_id[ott1059898,Intercept] 1.339869e+00
## r_species_id[ott1059900,Intercept] 1.370877e+00
## r_species_id[ott1061937,Intercept] 2.667318e-01
## r_species_id[ott1069171,Intercept] 1.272453e+00
## r_species_id[ott1072227,Intercept] 3.919902e-01
## r_species_id[ott108923,Intercept] 1.376201e+00
## r_species_id[ott1099013,Intercept] 2.405201e-01
## r_species_id[ott111442,Intercept] 1.585876e+00
## r_species_id[ott112015,Intercept] 4.109330e-01
## r_species_id[ott112016,Intercept] 4.217453e-01
## r_species_id[ott112017,Intercept] 4.003435e-01
## r_species_id[ott127047,Intercept] 1.343197e+00
## r_species_id[ott150272,Intercept] 1.697914e+00
## r_species_id[ott160850,Intercept] 4.689000e-01
## r_species_id[ott165368,Intercept] 2.293505e+00
## r_species_id[ott167121,Intercept] 3.469349e-01
## r_species_id[ott178177,Intercept] 8.167691e-01
## r_species_id[ott178412,Intercept] 1.130074e+00
## r_species_id[ott181933,Intercept] 3.862495e-01
## r_species_id[ott182906,Intercept] 1.798972e+00
## r_species_id[ott186999,Intercept] 1.066171e+00
## r_species_id[ott187583,Intercept] -3.961844e-02
## r_species_id[ott199292,Intercept] 1.440691e+00
## r_species_id[ott207134,Intercept] 5.608879e-01
## r_species_id[ott215125,Intercept] 1.939675e+00
## r_species_id[ott216694,Intercept] -3.004971e-02
## r_species_id[ott223669,Intercept] 2.354876e+00
## r_species_id[ott225275,Intercept] 1.560557e+00
## r_species_id[ott237608,Intercept] 5.430362e-01
## r_species_id[ott246046,Intercept] 9.440899e-01
## r_species_id[ott247341,Intercept] 1.843081e+00
## r_species_id[ott256062,Intercept] 2.569962e-01
## r_species_id[ott256089,Intercept] 2.134858e-01
## r_species_id[ott256145,Intercept] 4.156694e-01
## r_species_id[ott263960,Intercept] 1.487395e+00
## r_species_id[ott263980,Intercept] 1.821700e-01
## r_species_id[ott263987,Intercept] 3.135872e-01
## r_species_id[ott263988,Intercept] 1.966490e-01
## r_species_id[ott265121,Intercept] 1.987558e-01
## r_species_id[ott266342,Intercept] -5.652386e-02
## r_species_id[ott269063,Intercept] 6.525535e-01
## r_species_id[ott275893,Intercept] 5.254201e-01
## r_species_id[ott275897,Intercept] 5.611716e-01
## r_species_id[ott2810724,Intercept] 5.205180e-01
## r_species_id[ott2819986,Intercept] 1.939251e-01
## r_species_id[ott2821097,Intercept] 3.058506e-01
## r_species_id[ott2844172,Intercept] 1.867019e+00
## r_species_id[ott2844962,Intercept] 1.683311e+00
## r_species_id[ott2849837,Intercept] 1.593173e+00
## r_species_id[ott2942244,Intercept] 1.488557e+00
## r_species_id[ott316441,Intercept] 1.740501e+00
## r_species_id[ott33153,Intercept] 4.733835e-01
## r_species_id[ott336388,Intercept] 2.140194e-01
## r_species_id[ott34559,Intercept] 2.427111e-01
## r_species_id[ott346740,Intercept] 1.477895e+00
## r_species_id[ott3583594,Intercept] 1.741328e+00
## r_species_id[ott3587677,Intercept] 2.076895e+00
## r_species_id[ott359012,Intercept] 4.622310e-01
## r_species_id[ott361837,Intercept] 4.113867e-01
## r_species_id[ott362913,Intercept] 1.657625e+00
## r_species_id[ott365439,Intercept] 1.205430e+00
## r_species_id[ott3663378,Intercept] 1.243605e+00
## r_species_id[ott3665433,Intercept] 1.602239e+00
## r_species_id[ott3684291,Intercept] 1.166057e+00
## r_species_id[ott3684365,Intercept] 1.163531e+00
## r_species_id[ott3684379,Intercept] 1.370347e+00
## r_species_id[ott3684389,Intercept] 1.241146e+00
## r_species_id[ott3684437,Intercept] 1.184740e+00
## r_species_id[ott381979,Intercept] 6.632714e-01
## r_species_id[ott381980,Intercept] 1.776814e-01
## r_species_id[ott381983,Intercept] 4.227690e-01
## r_species_id[ott395048,Intercept] 2.095412e+00
## r_species_id[ott3974169,Intercept] 1.808084e+00
## r_species_id[ott3995126,Intercept] 1.772068e+00
## r_species_id[ott4010019,Intercept] 6.828893e-01
## r_species_id[ott4010960,Intercept] 7.009474e-01
## r_species_id[ott4011155,Intercept] 8.725964e-01
## r_species_id[ott4013437,Intercept] 7.762375e-01
## r_species_id[ott4013674,Intercept] 6.155598e-01
## r_species_id[ott4013684,Intercept] 6.534482e-01
## r_species_id[ott422679,Intercept] 5.031977e-01
## r_species_id[ott431388,Intercept] 1.689261e+00
## r_species_id[ott446088,Intercept] 1.762837e+00
## r_species_id[ott4741377,Intercept] 7.315349e-01
## r_species_id[ott4742064,Intercept] 6.707836e-01
## r_species_id[ott481952,Intercept] 1.759286e+00
## r_species_id[ott48288,Intercept] 1.806502e+00
## r_species_id[ott485470,Intercept] 1.843978e-01
## r_species_id[ott485473,Intercept] 2.626416e-01
## r_species_id[ott485476,Intercept] 3.492260e-01
## r_species_id[ott485480,Intercept] 1.883389e-01
## r_species_id[ott485482,Intercept] -7.173396e-03
## r_species_id[ott486834,Intercept] 5.247811e-01
## r_species_id[ott490206,Intercept] 1.625513e+00
## r_species_id[ott492241,Intercept] 1.519779e+00
## r_species_id[ott497063,Intercept] 2.479729e-01
## r_species_id[ott4974308,Intercept] 1.395087e+00
## r_species_id[ott4978773,Intercept] 1.591340e+00
## r_species_id[ott4979583,Intercept] 1.524907e+00
## r_species_id[ott518643,Intercept] 1.358279e+00
## r_species_id[ott542509,Intercept] 2.159387e+00
## r_species_id[ott54768,Intercept] 1.407624e+00
## r_species_id[ott549846,Intercept] 1.219716e+00
## r_species_id[ott560703,Intercept] 6.130539e-01
## r_species_id[ott567703,Intercept] 6.961564e-01
## r_species_id[ott570365,Intercept] 1.301219e+00
## r_species_id[ott570656,Intercept] 1.523469e+00
## r_species_id[ott588761,Intercept] 1.622762e+00
## r_species_id[ott592355,Intercept] 1.089040e+00
## r_species_id[ott601255,Intercept] -5.187107e-02
## r_species_id[ott602180,Intercept] 7.153614e-01
## r_species_id[ott60470,Intercept] 4.063180e-01
## r_species_id[ott60471,Intercept] 6.454145e-01
## r_species_id[ott60473,Intercept] 4.279220e-01
## r_species_id[ott60477,Intercept] 3.509635e-01
## r_species_id[ott60479,Intercept] 3.508483e-01
## r_species_id[ott633708,Intercept] 3.132205e-01
## r_species_id[ott633710,Intercept] 1.875609e-01
## r_species_id[ott633711,Intercept] 1.236622e-01
## r_species_id[ott633717,Intercept] 4.216662e-01
## r_species_id[ott633719,Intercept] 1.902316e-01
## r_species_id[ott643237,Intercept] 1.852461e+00
## r_species_id[ott645555,Intercept] 4.194099e-01
## r_species_id[ott649193,Intercept] 1.523073e-02
## r_species_id[ott675301,Intercept] 1.992166e+00
## r_species_id[ott724784,Intercept] 6.899952e-01
## r_species_id[ott72522,Intercept] 4.054181e-01
## r_species_id[ott727979,Intercept] -5.614257e-02
## r_species_id[ott733462,Intercept] 9.703878e-01
## r_species_id[ott736728,Intercept] 5.901024e-01
## r_species_id[ott742128,Intercept] 1.415214e+00
## r_species_id[ott7489702,Intercept] 1.293004e+00
## r_species_id[ott7567530,Intercept] 1.349619e+00
## r_species_id[ott765113,Intercept] 5.330402e-01
## r_species_id[ott765280,Intercept] 1.427270e+00
## r_species_id[ott779028,Intercept] 6.041852e-01
## r_species_id[ott790395,Intercept] 2.080392e-01
## r_species_id[ott817791,Intercept] 3.403504e-01
## r_species_id[ott821356,Intercept] 1.588549e+00
## r_species_id[ott83430,Intercept] 2.647235e-01
## r_species_id[ott83432,Intercept] 2.839053e-01
## r_species_id[ott840001,Intercept] 1.854120e+00
## r_species_id[ott841027,Intercept] -6.355871e-02
## r_species_id[ott849781,Intercept] 1.955769e-01
## r_species_id[ott878345,Intercept] 2.879141e-01
## r_species_id[ott92556,Intercept] 4.974022e-01
## r_species_id[ott92561,Intercept] 4.099571e-01
## r_species_id[ott939432,Intercept] 2.598973e-01
## r_species_id[ott939454,Intercept] 2.639871e-01
## r_species_id[ott954042,Intercept] 1.167595e+00
## r_species_id[ott958293,Intercept] 2.822249e-01
## r_species_id[ott958304,Intercept] 4.169826e-01
## r_species_id[ott962359,Intercept] 4.596690e-01
## r_species_id[ott987480,Intercept] 2.040528e+00
## r_species_id[ott989764,Intercept] 9.509299e-01
## lp__ -5.716001e+02plot(conditional_effects(fit_type_phy, points = TRUE, ask = F)) 
hyp = hypothesis(fit_type_phy, c("germline_timing_simpleearly = germline_timing_simple", "germline_timing_simpleearly = germline_timing_simpleadult", "germline_timing_simpleearly = germline_timing_simpleno_germline"))
hyp## Hypothesis Tests for class b:
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio
## 1 (germline_timing_... = 0 0.61 0.27 0.11 1.15 NA
## 2 (germline_timing_... = 0 0.53 0.21 0.13 0.93 NA
## 3 (germline_timing_... = 0 1.05 0.33 0.41 1.72 NA
## Post.Prob Star
## 1 NA *
## 2 NA *
## 3 NA *
## ---
## 'CI': 90%-CI for one-sided and 95%-CI for two-sided hypotheses.
## '*': For one-sided hypotheses, the posterior probability exceeds 95%;
## for two-sided hypotheses, the value tested against lies outside the 95%-CI.
## Posterior probabilities of point hypotheses assume equal prior probabilities.plot(hyp)